Datasets:
Tasks:
Text2Text Generation
Sub-tasks:
abstractive-qa
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
crowdsourced
Source Datasets:
original
ArXiv:
License:
| # 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") | |
| 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( | |
| "{} 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 | |