Datasets:
hover

	# 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