Datasets:
Tasks:
Text Classification
Languages:
English
Size:
100K<n<1M
ArXiv:
Tags:
knowledge-verification
License:
Add dataset loading script
Browse files- feverous.py +126 -0
feverous.py
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"""FEVEROUS dataset."""
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import json
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import textwrap
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import datasets
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class FeverousConfig(datasets.BuilderConfig):
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"""BuilderConfig for FEVER."""
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def __init__(self, homepage: str = None, citation: str = None, base_url: str = None, urls: dict = None, **kwargs):
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"""BuilderConfig for FEVEROUS.
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Args:
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homepage (`str`): Homepage.
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citation (`str`): Citation reference.
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base_url (`str`): Data base URL that precedes all data URLs.
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urls (`dict`): Data URLs (each URL will pe preceded by `base_url`).
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**kwargs: keyword arguments forwarded to super.
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"""
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super().__init__(**kwargs)
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self.homepage = homepage
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self.citation = citation
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self.base_url = base_url
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self.urls = {key: f"{base_url}/{url}" for key, url in urls.items()}
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class FeverOUS(datasets.GeneratorBasedBuilder):
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"""FEVEROUS dataset."""
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BUILDER_CONFIGS = [
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FeverousConfig(
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version=datasets.Version("1.0.0"),
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description=textwrap.dedent(
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"FEVEROUS:\n"
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"FEVEROUS (Fact Extraction and VERification Over Unstructured and Structured information) is a fact "
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"verification dataset which consists of 87,026 verified claims. Each claim is annotated with evidence "
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"in the form of sentences and/or cells from tables in Wikipedia, as well as a label indicating whether "
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"this evidence supports, refutes, or does not provide enough information to reach a verdict. The "
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"dataset also contains annotation metadata such as annotator actions (query keywords, clicks on page, "
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"time signatures), and the type of challenge each claim poses."
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),
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homepage="https://fever.ai/dataset/feverous.html",
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citation=textwrap.dedent(
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"""\
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@inproceedings{Aly21Feverous,
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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},
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title = {{FEVEROUS}: Fact Extraction and {VERification} Over Unstructured and Structured information},
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eprint={2106.05707},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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year = {2021}
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}"""
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),
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base_url="https://fever.ai/download/feverous",
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urls={
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datasets.Split.TRAIN: "feverous_train_challenges.jsonl",
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datasets.Split.VALIDATION: "feverous_dev_challenges.jsonl",
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datasets.Split.TEST: "feverous_test_unlabeled.jsonl",
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},
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),
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]
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def _info(self):
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features = {
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"id": datasets.Value("int32"),
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"label": datasets.ClassLabel(names=["SUPPORTS", "REFUTES", "NOT ENOUGH INFO"]),
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"claim": datasets.Value("string"),
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"evidence": [
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{
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"content": [datasets.Value("string")],
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"context": [[datasets.Value("string")]],
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}
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],
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"annotator_operations": [
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{
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"operation": datasets.Value("string"),
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"value": datasets.Value("string"),
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"time": datasets.Value("float"),
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}
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],
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"expected_challenge": datasets.Value("string"),
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"challenge": datasets.Value("string"),
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}
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return datasets.DatasetInfo(
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description=self.config.description,
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features=datasets.Features(features),
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homepage=self.config.homepage,
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citation=self.config.citation,
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)
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def _split_generators(self, dl_manager):
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dl_paths = dl_manager.download_and_extract(self.config.urls)
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return [
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datasets.SplitGenerator(
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name=split,
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gen_kwargs={
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"filepath": dl_paths[split],
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},
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)
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for split in dl_paths.keys()
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]
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def _generate_examples(self, filepath):
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with open(filepath, encoding="utf-8") as f:
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for id_, row in enumerate(f):
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data = json.loads(row)
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# First item in "train" has all values equal to empty strings
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if [value for value in data.values() if value]:
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evidence = data.get("evidence", [])
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if evidence:
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for evidence_set in evidence:
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# Transform "context" from dict to list (analogue to "content")
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evidence_set["context"] = [
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evidence_set["context"][element_id] for element_id in evidence_set["content"]
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]
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yield id_, {
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"id": data.get("id"),
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"label": data.get("label", -1),
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"claim": data.get("claim", ""),
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"evidence": evidence,
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"annotator_operations": data.get("annotator_operations", []),
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"expected_challenge": data.get("expected_challenge", ""),
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"challenge": data.get("challenge", ""),
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}
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