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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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
"""SciFact Dataset (Retrieval Only)"""
import json
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 = "dataset load script for SciFact"
_DATASET_URLS = {
'train': "https://huggingface.co/datasets/Tevatron/scifact/resolve/main/train.jsonl.gz",
'dev': "https://huggingface.co/datasets/Tevatron/scifact/resolve/main/dev.jsonl.gz",
'test': "https://huggingface.co/datasets/Tevatron/scifact/resolve/main/test.jsonl.gz",
}
class Scifact(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("0.0.1")
BUILDER_CONFIGS = [
datasets.BuilderConfig(version=VERSION,
description="SciFact train/dev/test datasets"),
]
def _info(self):
features = datasets.Features({
'query_id': datasets.Value('string'),
'query': datasets.Value('string'),
'positive_passages': [
{'docid': datasets.Value('string'), 'text': datasets.Value('string'),
'title': datasets.Value('string')}
],
'negative_passages': [
{'docid': datasets.Value('string'), 'text': datasets.Value('string'),
'title': datasets.Value('string')}
],
})
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
supervised_keys=None,
# Homepage of the dataset for documentation
homepage="",
# License for the dataset if available
license="",
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
downloaded_files = dl_manager.download_and_extract(_DATASET_URLS)
splits = [
datasets.SplitGenerator(
name="train",
gen_kwargs={
"filepath": downloaded_files["train"],
},
),
datasets.SplitGenerator(
name='dev',
gen_kwargs={
"filepath": downloaded_files["dev"],
},
),
datasets.SplitGenerator(
name='test',
gen_kwargs={
"filepath": downloaded_files["test"],
},
),
]
return splits
def _generate_examples(self, filepath):
"""Yields examples."""
with open(filepath, encoding="utf-8") as f:
for line in f:
data = json.loads(line)
if data.get('negative_passages') is None:
data['negative_passages'] = []
if data.get('positive_passages') is None:
data['positive_passages'] = []
yield data['query_id'], data
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