Datasets:

deepmind
/

narrativeqa

	# 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.
	"""NarrativeQA Reading Comprehension Challenge"""

	from __future__ import absolute_import, division, print_function

	import csv
	import os

	import datasets


	_CITATION = """\
	@article{narrativeqa,
	author = {Tom\\'a\\v s Ko\\v cisk\\'y and Jonathan Schwarz and Phil Blunsom and
	Chris Dyer and Karl Moritz Hermann and G\\'abor Melis and
	Edward Grefenstette},
	title = {The {NarrativeQA} Reading Comprehension Challenge},
	journal = {Transactions of the Association for Computational Linguistics},
	url = {https://TBD},
	volume = {TBD},
	year = {2018},
	pages = {TBD},
	}
	"""

	_DESCRIPTION = """\
	The NarrativeQA dataset for question answering on long documents (movie scripts, books). It includes the list of documents with Wikipedia summaries, links to full stories, and questions and answers.
	"""

	_URLS = {
	"full_text": "https://storage.googleapis.com/huggingface-nlp/datasets/narrative_qa/narrativeqa_full_text.zip",
	"repo": "https://github.com/deepmind/narrativeqa/archive/master.zip",
	}


	class NarrativeQa(datasets.GeneratorBasedBuilder):
	"""NarrativeQA: Question answering on long-documents"""

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	citation=_CITATION,
	features=datasets.Features(
	{
	"document": {
	"id": datasets.Value("string"),
	"kind": datasets.Value("string"),
	"url": datasets.Value("string"),
	"file_size": datasets.Value("int32"),
	"word_count": datasets.Value("int32"),
	"start": datasets.Value("string"),
	"end": datasets.Value("string"),
	"summary": {
	"text": datasets.Value("string"),
	"tokens": datasets.features.Sequence(datasets.Value("string")),
	"url": datasets.Value("string"),
	"title": datasets.Value("string"),
	},
	"text": datasets.Value("string"),
	},
	"question": {
	"text": datasets.Value("string"),
	"tokens": datasets.features.Sequence(datasets.Value("string")),
	},
	"answers": [
	{
	"text": datasets.Value("string"),
	"tokens": datasets.features.Sequence(datasets.Value("string")),
	}
	],
	}
	),
	homepage="https://github.com/deepmind/narrativeqa",
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""

	dl_dir = dl_manager.download_and_extract(_URLS)
	dl_dir["repo"] = os.path.join(dl_dir["repo"], "narrativeqa-master")

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"repo_dir": dl_dir["repo"], "full_text_dir": dl_dir["full_text"], "split": "train"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"repo_dir": dl_dir["repo"], "full_text_dir": dl_dir["full_text"], "split": "test"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"repo_dir": dl_dir["repo"], "full_text_dir": dl_dir["full_text"], "split": "valid"},
	),
	]

	def _generate_examples(self, repo_dir, full_text_dir, split):
	"""Yields examples."""
	documents = {}
	with open(os.path.join(repo_dir, "documents.csv"), encoding="utf-8") as f:
	reader = csv.DictReader(f)
	for row in reader:
	if row["set"] != split:
	continue
	documents[row["document_id"]] = row

	summaries = {}
	with open(os.path.join(repo_dir, "third_party", "wikipedia", "summaries.csv"), encoding="utf-8") as f:
	reader = csv.DictReader(f)
	for row in reader:
	if row["set"] != split:
	continue
	summaries[row["document_id"]] = row

	with open(os.path.join(repo_dir, "qaps.csv"), encoding="utf-8") as f:
	reader = csv.DictReader(f)
	for id_, row in enumerate(reader):
	if row["set"] != split:
	continue
	document_id = row["document_id"]
	document = documents[document_id]
	summary = summaries[document_id]
	full_text = open(os.path.join(full_text_dir, document_id + ".content"), encoding="latin-1").read()
	res = {
	"document": {
	"id": document["document_id"],
	"kind": document["kind"],
	"url": document["story_url"],
	"file_size": document["story_file_size"],
	"word_count": document["story_word_count"],
	"start": document["story_start"],
	"end": document["story_end"],
	"summary": {
	"text": summary["summary"],
	"tokens": summary["summary_tokenized"].split(),
	"url": document["wiki_url"],
	"title": document["wiki_title"],
	},
	"text": full_text,
	},
	"question": {"text": row["question"], "tokens": row["question_tokenized"].split()},
	"answers": [
	{"text": row["answer1"], "tokens": row["answer1_tokenized"].split()},
	{"text": row["answer2"], "tokens": row["answer2_tokenized"].split()},
	],
	}
	yield id_, res