Datasets:

wenhu
/

hybrid_qa

	# coding=utf-8
	# Copyright 2020 The 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.
	"""HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data"""


	import json
	import os

	import datasets


	_CITATION = """\
	@article{chen2020hybridqa,
	title={HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data},
	author={Chen, Wenhu and Zha, Hanwen and Chen, Zhiyu and Xiong, Wenhan and Wang, Hong and Wang, William},
	journal={Findings of EMNLP 2020},
	year={2020}
	}
	"""

	_DESCRIPTION = """\
	Existing question answering datasets focus on dealing with homogeneous information, based either only on text or \
	KB/Table information alone. However, as human knowledge is distributed over heterogeneous forms, \
	using homogeneous information alone might lead to severe coverage problems. \
	To fill in the gap, we present HybridQA, a new large-scale question-answering dataset that \
	requires reasoning on heterogeneous information. Each question is aligned with a Wikipedia table \
	and multiple free-form corpora linked with the entities in the table. The questions are designed \
	to aggregate both tabular information and text information, i.e., \
	lack of either form would render the question unanswerable.
	"""

	_HOMEPAGE = "https://hybridqa.github.io/index.html"

	_WIKI_TABLES_GIT_ARCHIVE_URL = "WikiTables-WithLinks-f4ed68e54e25c495f63d309de0b89c0f97b3c508.zip"

	_QA_DATA_BASE_URL = "https://raw.githubusercontent.com/wenhuchen/HybridQA/master/released_data"
	_URLS = {
	"train": f"{_QA_DATA_BASE_URL}/train.json",
	"dev": f"{_QA_DATA_BASE_URL}/dev.json",
	"test": f"{_QA_DATA_BASE_URL}/test.json",
	}


	class HybridQa(datasets.GeneratorBasedBuilder):
	"""HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data"""

	VERSION = datasets.Version("1.0.0")
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="hybrid_qa",
	version=datasets.Version("1.0.0"),
	),
	]

	def _info(self):
	features = {
	"question_id": datasets.Value("string"),
	"question": datasets.Value("string"),
	"table_id": datasets.Value("string"),
	"answer_text": datasets.Value("string"),
	"question_postag": datasets.Value("string"),
	"table": {
	"url": datasets.Value("string"),
	"title": datasets.Value("string"),
	"header": datasets.Sequence(datasets.Value("string")),
	"data": [
	{
	"value": datasets.Value("string"),
	"urls": [{"url": datasets.Value("string"), "summary": datasets.Value("string")}],
	}
	],
	"section_title": datasets.Value("string"),
	"section_text": datasets.Value("string"),
	"uid": datasets.Value("string"),
	"intro": datasets.Value("string"),
	},
	}
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(features),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	extracted_path = dl_manager.download_and_extract(_WIKI_TABLES_GIT_ARCHIVE_URL)
	downloaded_files = dl_manager.download(_URLS)

	repo_path = os.path.join(extracted_path, "WikiTables-WithLinks-f4ed68e54e25c495f63d309de0b89c0f97b3c508")
	tables_path = os.path.join(repo_path, "tables_tok")
	requests_path = os.path.join(repo_path, "request_tok")

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"qa_filepath": downloaded_files["train"],
	"tables_path": tables_path,
	"requests_path": requests_path,
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"qa_filepath": downloaded_files["dev"],
	"tables_path": tables_path,
	"requests_path": requests_path,
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"qa_filepath": downloaded_files["test"],
	"tables_path": tables_path,
	"requests_path": requests_path,
	},
	),
	]

	def _generate_examples(self, qa_filepath, tables_path, requests_path):
	with open(qa_filepath, encoding="utf-8") as f:
	examples = json.load(f)

	for example in examples:
	table_id = example["table_id"]
	table_file_path = os.path.join(tables_path, f"{table_id}.json")
	url_data_path = os.path.join(requests_path, f"{table_id}.json")

	try:
	with open(table_file_path, encoding="utf-8") as f:
	table = json.load(f)
	with open(url_data_path, encoding="utf-8") as f:
	url_data = json.load(f)
	except FileNotFoundError: # Some JSON files were not properly added to the GitHub repo: filenames with ':', '"'
	continue

	table["header"] = [header[0] for header in table["header"]]

	# here each row is a list with two elemets, the row value and list of urls for that row
	# convert it to list of dict with keys value and urls
	rows = []
	for row in table["data"]:
	for col in row:
	new_row = {"value": col[0]}
	urls = col[1]
	new_row["urls"] = [{"url": url, "summary": url_data[url]} for url in urls]
	rows.append(new_row)
	table["data"] = rows

	example["answer_text"] = example.pop("answer-text") if "answer-text" in example else ""
	example["table"] = table

	yield example["question_id"], example