Datasets:
Tasks:
Text Retrieval
Sub-tasks:
fact-checking-retrieval
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
Tags:
License:
File size: 4,476 Bytes
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# coding=utf-8
# Copyright 2020 HuggingFace Datasets Authors.
#
# 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
import json
import datasets
_DESCRIPTION = """\
HoVer is an open-domain, many-hop fact extraction and claim verification dataset built upon the Wikipedia corpus. The original 2-hop claims are adapted from question-answer pairs from HotpotQA. It is collected by a team of NLP researchers at UNC Chapel Hill and Verisk Analytics.
"""
_HOMEPAGE_URL = "https://hover-nlp.github.io/"
_CITATION = """\
@inproceedings{jiang2020hover,
title={{HoVer}: A Dataset for Many-Hop Fact Extraction And Claim Verification},
author={Yichen Jiang and Shikha Bordia and Zheng Zhong and Charles Dognin and Maneesh Singh and Mohit Bansal.},
booktitle={Findings of the Conference on Empirical Methods in Natural Language Processing ({EMNLP})},
year={2020}
}
"""
_TRAIN_URL = "https://raw.githubusercontent.com/hover-nlp/hover/main/data/hover/hover_train_release_v1.1.json"
_VALID_URL = "https://raw.githubusercontent.com/hover-nlp/hover/main/data/hover/hover_dev_release_v1.1.json"
_TEST_URL = "https://raw.githubusercontent.com/hover-nlp/hover/main/data/hover/hover_test_release_v1.1.json"
class Hover(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.1.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("int32"),
"uid": datasets.Value("string"),
"claim": datasets.Value("string"),
"supporting_facts": [
{
"key": datasets.Value("string"),
"value": datasets.Value("int32"),
}
],
"label": datasets.ClassLabel(names=["NOT_SUPPORTED", "SUPPORTED"]),
"num_hops": datasets.Value("int32"),
"hpqa_id": datasets.Value("string"),
},
),
supervised_keys=None,
homepage=_HOMEPAGE_URL,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
train_path = dl_manager.download_and_extract(_TRAIN_URL)
valid_path = dl_manager.download_and_extract(_VALID_URL)
test_path = dl_manager.download_and_extract(_TEST_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"datapath": train_path, "datatype": "train"},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"datapath": valid_path, "datatype": "valid"},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"datapath": test_path, "datatype": "test"},
),
]
def _generate_examples(self, datapath, datatype):
with open(datapath, encoding="utf-8") as f:
data = json.load(f)
for sentence_counter, d in enumerate(data):
if datatype != "test":
resp = {
"id": sentence_counter,
"uid": d["uid"],
"claim": d["claim"],
"supporting_facts": [{"key": x[0], "value": x[1]} for x in d["supporting_facts"]],
"label": d["label"],
"num_hops": d["num_hops"],
"hpqa_id": d["hpqa_id"],
}
else:
resp = {
"id": sentence_counter,
"uid": d["uid"],
"claim": d["claim"],
"supporting_facts": [],
"label": -1,
"num_hops": -1,
"hpqa_id": "None",
}
yield sentence_counter, resp
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