Datasets:
Tasks:
Text2Text Generation
Modalities:
Text
Formats:
parquet
Sub-tasks:
abstractive-qa
Languages:
English
Size:
10K - 100K
ArXiv:
License:
File size: 6,767 Bytes
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# 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"""
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.
"""
# Source:
# - full_text: https://storage.googleapis.com/huggingface-nlp/datasets/narrative_qa/narrativeqa_full_text.zip
# - repo: https://github.com/deepmind/narrativeqa/archive/master.zip
_URLS = {
"full_text": "data/narrativeqa_full_text.zip",
"repo": "data/narrativeqa-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
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