# 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
from os import listdir
from os.path import isfile, join
import datasets
_CITATION = """\
@article{kovcisky2018narrativeqa,
title={The narrativeqa reading comprehension challenge},
author={Ko{\v{c}}isk{\'y}, Tom{\'a}{\v{s}} and Schwarz, Jonathan and Blunsom, Phil and Dyer, Chris and Hermann, Karl Moritz and Melis, G{\'a}bor and Grefenstette, Edward},
journal={Transactions of the Association for Computational Linguistics},
volume={6},
pages={317--328},
year={2018},
publisher={MIT Press}
}
"""
_DESCRIPTION = """\
The Narrative QA Manual dataset is a reading comprehension \
dataset, in which the reader must answer questions about stories \
by reading entire books or movie scripts. \
The QA tasks are designed so that successfully answering their questions \
requires understanding the underlying narrative rather than \
relying on shallow pattern matching or salience.\\
THIS DATASET REQUIRES A MANUALLY DOWNLOADED FILE! \
Because of a script in the original repository which downloads the stories from original URLs everytime, \
The links are sometimes broken or invalid. \
Therefore, you need to manually download the stories for this dataset using the script provided by the authors \
(https://github.com/deepmind/narrativeqa/blob/master/download_stories.sh). Running the shell script creates a folder named "tmp" \
in the root directory and downloads the stories there. This folder containing the stories\
can be used to load the dataset via `datasets.load_dataset("narrativeqa_manual", data_dir="<path/to/folder>")`. """
_HOMEPAGE = "https://deepmind.com/research/publications/narrativeqa-reading-comprehension-challenge"
_LICENSE = "https://github.com/deepmind/narrativeqa/blob/master/LICENSE"
# The HuggingFace dataset library don't host the datasets but only point to the original files
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URL = "https://github.com/deepmind/narrativeqa"
_URLS = {
"documents": "https://raw.githubusercontent.com/deepmind/narrativeqa/master/documents.csv",
"summaries": "https://raw.githubusercontent.com/deepmind/narrativeqa/master/third_party/wikipedia/summaries.csv",
"qaps": "https://raw.githubusercontent.com/deepmind/narrativeqa/master/qaps.csv",
}
class NarrativeqaManual(datasets.GeneratorBasedBuilder):
"""The NarrativeQA Manual dataset"""
VERSION = datasets.Version("1.0.0")
@property
def manual_download_instructions(self):
return """ You need to manually download the stories for this dataset using the script provided by the authors \
(https://github.com/deepmind/narrativeqa/blob/master/download_stories.sh). Running the shell script creates a folder named "tmp"\
in the root directory and downloads the stories there. This folder containing the stories\
can be used to load the dataset via `datasets.load_dataset("narrativeqa_manual", data_dir="<path/to/folder>")."""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
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")),
}
],
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
data_dir = dl_manager.download_and_extract(_URLS)
manual_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
if not os.path.exists(manual_dir):
raise FileNotFoundError(
f"{manual_dir} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('narrativeqa_manual', data_dir=...)` that includes the stories downloaded from the original repository. Manual download instructions: {self.manual_download_instructions}"
)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_dir": data_dir,
"manual_dir": manual_dir,
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"data_dir": data_dir,
"manual_dir": manual_dir,
"split": "test",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"data_dir": data_dir,
"manual_dir": manual_dir,
"split": "valid",
},
),
]
def _generate_examples(self, data_dir, manual_dir, split):
"""Yields examples."""
documents = {}
with open(data_dir["documents"], 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(data_dir["summaries"], encoding="utf-8") as f:
reader = csv.DictReader(f)
for row in reader:
if row["set"] != split:
continue
summaries[row["document_id"]] = row
onlyfiles = [f for f in listdir(manual_dir) if isfile(join(manual_dir, f))]
story_texts = {}
for i in onlyfiles:
if "content" in i:
with open(os.path.join(manual_dir, i), "r", encoding="utf-8", errors="ignore") as f:
text = f.read()
story_texts[i.split(".")[0]] = text
with open(data_dir["qaps"], 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 = story_texts[document_id]
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