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scifact.py
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scifact.py
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"""TODO(scifact): Add a description here."""
from __future__ import absolute_import, division, print_function
import json
import os
import nlp
# TODO(scifact): BibTeX citation
_CITATION = """\
@inproceedings{scifact2020
title={ Fact or Fiction: Verifying Scientific Claims},
author={David, Wadden and Kyle, Lo and Lucy Lu, Wang and Shanchuan, Lin and Madeleine van, Zuylen and Arman, Cohan and Hannaneh, Hajishirzi},
booktitle={2011 AAAI Spring Symposium Series},
year={2020},
}
"""
# TODO(scifact):
_DESCRIPTION = """\
SciFact, a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts, and annotated with labels and rationales
"""
_URL = "https://ai2-s2-scifact.s3-us-west-2.amazonaws.com/release/2020-05-01/data.tar.gz"
class ScifactConfig(nlp.BuilderConfig):
"""BuilderConfig for Scifact"""
def __init__(self, **kwargs):
"""
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(ScifactConfig, self).__init__(
version=nlp.Version("1.0.0", "New split API (https://tensorflow.org/datasets/splits)"), **kwargs
)
class Scifact(nlp.GeneratorBasedBuilder):
"""TODO(scifact): Short description of my dataset."""
# TODO(scifact): Set up version.
VERSION = nlp.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 nlp.DatasetInfo object
if self.config.name == "corpus":
features = {
"doc_id": nlp.Value("int32"), # The document's S2ORC ID.
"title": nlp.Value("string"), # The title.
"abstract": nlp.features.Sequence(
{"sentence": nlp.Value("string")}
), # The abstract, written as a list of sentences.
"structured": nlp.Value("bool"), # Indicator for whether this is a structured abstract.
}
else:
features = {
"id": nlp.Value("int32"), # An integer claim ID.
"claim": nlp.Value("string"), # The text of the claim.
"evidence_doc_id": nlp.Value("string"),
"evidence_label": nlp.Value("string"), # Label for the rationale.
"evidence_sentences": nlp.features.Sequence({"sentence": nlp.Value("int32")}), # Rationale sentences.
"cited_doc_ids": nlp.features.Sequence(
{"doc_id": nlp.Value("int32")}
), # The claim's "cited documents".
}
return nlp.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# nlp.features.FeatureConnectors
features=nlp.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 nlp.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 [
nlp.SplitGenerator(
name=nlp.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 [
nlp.SplitGenerator(
name=nlp.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(dl_dir, "data", "claims_train.jsonl"), "split": "train"},
),
nlp.SplitGenerator(
name=nlp.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(dl_dir, "data", "claims_test.jsonl"), "split": "test"},
),
nlp.SplitGenerator(
name=nlp.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) 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": {"sentence": data["abstract"]},
"structured": data["structured"],
}
else:
if split == "test":
yield id_, {
"id": data["id"],
"claim": data["claim"],
"evidence_doc_id": "",
"evidence_label": "",
"evidence_sentences": {"sentence": []},
"cited_doc_ids": {"doc_id": []},
}
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": {"sentence": evidence["sentences"]},
"cited_doc_ids": {"doc_id": data.get("cited_doc_ids", [])},
}
else:
yield id_, {
"id": data["id"],
"claim": data["claim"],
"evidence_doc_id": "",
"evidence_label": "",
"evidence_sentences": {"sentence": []},
"cited_doc_ids": {"doc_id": data.get("cited_doc_ids", [])},
}