File size: 7,264 Bytes
698223e 521533d 698223e 521533d 698223e 521533d 698223e 521533d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 |
"""Scientific fact-checking dataset. Verifies claims based on citation sentences
using evidence from the cited abstracts."""
import json
import os
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
dl_dir = dl_manager.download_and_extract(_URL)
if self.config.name == "corpus":
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(dl_dir, "data", "corpus.jsonl"), "split": "train"},
),
]
else:
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(dl_dir, "data", "claims_train.jsonl"), "split": "train"},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(dl_dir, "data", "claims_test.jsonl"), "split": "test"},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(dl_dir, "data", "claims_dev.jsonl"), "split": "dev"},
),
]
def _generate_examples(self, filepath, split):
"""Yields examples."""
# TODO(scifact): Yields (key, example) tuples from the dataset
with open(filepath, encoding="utf-8") as f:
for id_, row in enumerate(f):
data = json.loads(row)
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", []),
}
|