# 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 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="")`. """ _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="").""" 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( "{} 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: {}".format( manual_dir, 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