fever / fever.py
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# Modified by Nora Belrose of EleutherAI (2023)
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# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
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# http://www.apache.org/licenses/LICENSE-2.0
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# 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