Datasets:

EleutherAI
/

fever

	# Modified by Nora Belrose of EleutherAI (2023)
	# coding=utf-8
	# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
	#
	# 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.

	# Lint as: python3
	"""FEVER dataset."""

	import json
	import os
	import textwrap

	import datasets


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

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

	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 Fever(datasets.GeneratorBasedBuilder):
	"""Fact Extraction and VERification Dataset."""

	BUILDER_CONFIGS = [
	FeverConfig(
	name="v1.0",
	version=datasets.Version("1.0.0"),
	description=textwrap.dedent(
	"FEVER v1.0\n"
	"FEVER (Fact Extraction and VERification) consists of 185,445 claims generated by altering sentences "
	"extracted from Wikipedia and subsequently verified without knowledge of the sentence they were "
	"derived from. The claims are classified as Supported, Refuted or NotEnoughInfo. For the first two "
	"classes, the annotators also recorded the sentence(s) forming the necessary evidence for their "
	"judgment."
	),
	homepage="https://fever.ai/dataset/fever.html",
	citation=textwrap.dedent(
	"""\
	@inproceedings{Thorne18Fever,
	author = {Thorne, James and Vlachos, Andreas and Christodoulopoulos, Christos and Mittal, Arpit},
	title = {{FEVER}: a Large-scale Dataset for Fact Extraction and {VERification}},
	booktitle = {NAACL-HLT},
	year = {2018}
	}"""
	),
	base_url="https://fever.ai/download/fever",
	urls={
	datasets.Split.TRAIN: "train.jsonl",
	"dev": "shared_task_dev.jsonl",
	"paper_dev": "paper_dev.jsonl",
	"paper_test": "paper_test.jsonl",
	},
	),
	FeverConfig(
	name="v2.0",
	version=datasets.Version("2.0.0"),
	description=textwrap.dedent(
	"FEVER v2.0:\n"
	"The FEVER 2.0 Dataset consists of 1174 claims created by the submissions of participants in the "
	"Breaker phase of the 2019 shared task. Participants (Breakers) were tasked with generating "
	"adversarial examples that induce classification errors for the existing systems. Breakers submitted "
	"a dataset of up to 1000 instances with equal number of instances for each of the three classes "
	"(Supported, Refuted NotEnoughInfo). Only novel claims (i.e. not contained in the original FEVER "
	"dataset) were considered as valid entries to the shared task. The submissions were then manually "
	"evaluated for Correctness (grammatical, appropriately labeled and meet the FEVER annotation "
	"guidelines requirements)."
	),
	homepage="https://fever.ai/dataset/adversarial.html",
	citation=textwrap.dedent(
	"""\
	@inproceedings{Thorne19FEVER2,
	author = {Thorne, James and Vlachos, Andreas and Cocarascu, Oana and Christodoulopoulos, Christos and Mittal, Arpit},
	title = {The {FEVER2.0} Shared Task},
	booktitle = {Proceedings of the Second Workshop on {Fact Extraction and VERification (FEVER)}},
	year = {2018}
	}"""
	),
	base_url="https://fever.ai/download/fever2.0",
	urls={
	datasets.Split.VALIDATION: "fever2-fixers-dev.jsonl",
	},
	),
	FeverConfig(
	name="wiki_pages",
	version=datasets.Version("1.0.0"),
	description=textwrap.dedent(
	"Wikipedia pages for FEVER v1.0:\n"
	"FEVER (Fact Extraction and VERification) consists of 185,445 claims generated by altering sentences "
	"extracted from Wikipedia and subsequently verified without knowledge of the sentence they were "
	"derived from. The claims are classified as Supported, Refuted or NotEnoughInfo. For the first two "
	"classes, the annotators also recorded the sentence(s) forming the necessary evidence for their "
	"judgment."
	),
	homepage="https://fever.ai/dataset/fever.html",
	citation=textwrap.dedent(
	"""\
	@inproceedings{Thorne18Fever,
	author = {Thorne, James and Vlachos, Andreas and Christodoulopoulos, Christos and Mittal, Arpit},
	title = {{FEVER}: a Large-scale Dataset for Fact Extraction and {VERification}},
	booktitle = {NAACL-HLT},
	year = {2018}
	}"""
	),
	base_url="https://fever.ai/download/fever",
	urls={
	"wikipedia_pages": "wiki-pages.zip",
	},
	),
	]

	def _info(self):
	if self.config.name == "wiki_pages":
	features = {
	"id": datasets.Value("string"),
	"text": datasets.Value("string"),
	"lines": datasets.Value("string"),
	}
	elif self.config.name == "v1.0" or self.config.name == "v2.0":
	features = {
	"id": datasets.Value("int32"),
	"label": datasets.ClassLabel(names=["REFUTES", "SUPPORTS"]),
	"claim": datasets.Value("string"),
	"evidence_annotation_id": datasets.Value("int32"),
	"evidence_id": datasets.Value("int32"),
	"evidence_wiki_url": datasets.Value("string"),
	"evidence_sentence_id": datasets.Value("int32"),
	}
	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):
	"""Returns SplitGenerators."""
	dl_paths = dl_manager.download_and_extract(self.config.urls)
	return [
	datasets.SplitGenerator(
	name=split,
	gen_kwargs={
	"filepath": dl_paths[split]
	if self.config.name != "wiki_pages"
	else dl_manager.iter_files(os.path.join(dl_paths[split], "wiki-pages")),
	},
	)
	for split in dl_paths.keys()
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""
	if self.config.name == "v1.0" or self.config.name == "v2.0":
	with open(filepath, encoding="utf-8") as f:
	for row_id, row in enumerate(f):
	data = json.loads(row)
	id_ = data["id"]
	label = data.get("label", "")

	# Drop the examples with label "NOT ENOUGH INFO"
	if label not in ("REFUTES", "SUPPORTS"):
	continue

	claim = data["claim"]
	evidences = data.get("evidence", [])
	if len(evidences) > 0:
	for i in range(len(evidences)):
	for j in range(len(evidences[i])):
	annot_id = evidences[i][j][0] if evidences[i][j][0] else -1
	evidence_id = evidences[i][j][1] if evidences[i][j][1] else -1
	wiki_url = evidences[i][j][2] if evidences[i][j][2] else ""
	sent_id = evidences[i][j][3] if evidences[i][j][3] else -1
	yield str(row_id) + "_" + str(i) + "_" + str(j), {
	"id": id_,
	"label": label,
	"claim": claim,
	"evidence_annotation_id": annot_id,
	"evidence_id": evidence_id,
	"evidence_wiki_url": wiki_url,
	"evidence_sentence_id": sent_id,
	}
	else:
	yield row_id, {
	"id": id_,
	"label": label,
	"claim": claim,
	"evidence_annotation_id": -1,
	"evidence_id": -1,
	"evidence_wiki_url": "",
	"evidence_sentence_id": -1,
	}
	elif self.config.name == "wiki_pages":
	for file_id, file in enumerate(filepath):
	with open(file, encoding="utf-8") as f:
	for row_id, row in enumerate(f):
	data = json.loads(row)
	yield f"{file_id}_{row_id}", data