Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
10M<n<100M
Language Creators:
found
Annotations Creators:
no-annotation
Source Datasets:
original
ArXiv:
License:
unknown
| # coding=utf-8 | |
| # Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors. | |
| # | |
| # 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. | |
| # Lint as: python3 | |
| """The BookCorpus dataset.""" | |
| import datasets | |
| _DESCRIPTION = """\ | |
| Books are a rich source of both fine-grained information, how a character, \ | |
| an object or a scene looks like, as well as high-level semantics, what \ | |
| someone is thinking, feeling and how these states evolve through a story.\ | |
| This work aims to align books to their movie releases in order to provide\ | |
| rich descriptive explanations for visual content that go semantically far\ | |
| beyond the captions available in current datasets. \ | |
| """ | |
| _CITATION = """\ | |
| @InProceedings{Zhu_2015_ICCV, | |
| title = {Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books}, | |
| author = {Zhu, Yukun and Kiros, Ryan and Zemel, Rich and Salakhutdinov, Ruslan and Urtasun, Raquel and Torralba, Antonio and Fidler, Sanja}, | |
| booktitle = {The IEEE International Conference on Computer Vision (ICCV)}, | |
| month = {December}, | |
| year = {2015} | |
| } | |
| """ | |
| URL = "https://storage.googleapis.com/huggingface-nlp/datasets/bookcorpus/bookcorpus.tar.bz2" | |
| class BookcorpusConfig(datasets.BuilderConfig): | |
| """BuilderConfig for BookCorpus.""" | |
| def __init__(self, **kwargs): | |
| """BuilderConfig for BookCorpus. | |
| Args: | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| super(BookcorpusConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs) | |
| class Bookcorpus(datasets.GeneratorBasedBuilder): | |
| """BookCorpus dataset.""" | |
| BUILDER_CONFIGS = [ | |
| BookcorpusConfig( | |
| name="plain_text", | |
| description="Plain text", | |
| ) | |
| ] | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "text": datasets.Value("string"), | |
| } | |
| ), | |
| supervised_keys=None, | |
| homepage="https://yknzhu.wixsite.com/mbweb", | |
| citation=_CITATION, | |
| ) | |
| def _vocab_text_gen(self, archive): | |
| for _, ex in self._generate_examples(archive): | |
| yield ex["text"] | |
| def _split_generators(self, dl_manager): | |
| arch_path = dl_manager.download(URL) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, gen_kwargs={"files": dl_manager.iter_archive(arch_path)} | |
| ), | |
| ] | |
| def _generate_examples(self, files): | |
| _id = 0 | |
| for path, file in files: | |
| for line in file: | |
| yield _id, {"text": line.decode("utf-8").strip()} | |
| _id += 1 | |