Datasets:

sewon
/

ambig_qa

	# 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.

	"""AmbigQA: Answering Ambiguous Open-domain Questions"""

	from __future__ import absolute_import, division, print_function

	import json
	import os

	import datasets


	_CITATION = """\
	@inproceedings{ min2020ambigqa,
	title={ {A}mbig{QA}: Answering Ambiguous Open-domain Questions },
	author={ Min, Sewon and Michael, Julian and Hajishirzi, Hannaneh and Zettlemoyer, Luke },
	booktitle={ EMNLP },
	year={2020}
	}
	"""

	_DESCRIPTION = """\
	AmbigNQ, a dataset covering 14,042 questions from NQ-open, an existing open-domain QA benchmark. We find that over half of the questions in NQ-open are ambiguous. The types of ambiguity are diverse and sometimes subtle, many of which are only apparent after examining evidence provided by a very large text corpus. AMBIGNQ, a dataset with
	14,042 annotations on NQ-OPEN questions containing diverse types of ambiguity.
	We provide two distributions of our new dataset AmbigNQ: a full version with all annotation metadata and a light version with only inputs and outputs.
	"""
	_HOMEPAGE = "https://nlp.cs.washington.edu/ambigqa/"
	_LICENSE = "CC BY-SA 3.0"

	_URL = "https://nlp.cs.washington.edu/ambigqa/data/"
	_URLS = {
	"light": _URL + "ambignq_light.zip",
	"full": _URL + "ambignq.zip",
	}


	class AmbigQa(datasets.GeneratorBasedBuilder):
	"""AmbigQA dataset"""

	VERSION = datasets.Version("1.0.0")
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="light",
	version=VERSION,
	description="AmbigNQ light version with only inputs and outputs",
	),
	datasets.BuilderConfig(
	name="full",
	version=VERSION,
	description="AmbigNQ full version with all annotation metadata",
	),
	]
	DEFAULT_CONFIG_NAME = "full"

	def _info(self):
	features_dict = {
	"id": datasets.Value("string"),
	"question": datasets.Value("string"),
	"annotations": datasets.features.Sequence(
	{
	"type": datasets.Value("string"), # datasets.ClassLabel(names = ["singleAnswer","multipleQAs"])
	"answer": datasets.features.Sequence(datasets.Value("string")),
	"qaPairs": datasets.features.Sequence(
	{
	"question": datasets.Value("string"),
	"answer": datasets.features.Sequence(datasets.Value("string")),
	}
	),
	}
	),
	}
	if self.config.name == "full":

	detail_features = {
	"viewed_doc_titles": datasets.features.Sequence(datasets.Value("string")),
	"used_queries": datasets.features.Sequence(
	{
	"query": datasets.Value("string"),
	"results": datasets.features.Sequence(
	{
	"title": datasets.Value("string"),
	"snippet": datasets.Value("string"),
	}
	),
	}
	),
	"nq_answer": datasets.features.Sequence(datasets.Value("string")),
	"nq_doc_title": datasets.Value("string"),
	}
	features_dict.update(detail_features)

	features = datasets.Features(features_dict)

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# download and extract URLs
	urls_to_download = _URLS
	downloaded_files = dl_manager.download_and_extract(urls_to_download)

	train_file_name = "train.json" if self.config.name == "full" else "train_light.json"
	dev_file_name = "dev.json" if self.config.name == "full" else "dev_light.json"

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"filepath": os.path.join(downloaded_files[self.config.name], train_file_name)},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"filepath": os.path.join(downloaded_files[self.config.name], dev_file_name)},
	),
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""

	with open(filepath, encoding="utf-8") as f:
	data = json.load(f)
	for example in data:
	id_ = example["id"]
	annotations = example["annotations"]
	# Add this because we cannot have None values (all keys in the schema should be present)
	for an in annotations:
	if "qaPairs" not in an:
	an["qaPairs"] = []
	if "answer" not in an:
	an["answer"] = []

	yield id_, example