Datasets:
Tasks:
Text2Text Generation
Modalities:
Text
Formats:
parquet
Sub-tasks:
abstractive-qa
Languages:
English
Size:
10K - 100K
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 | |
| 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 | |