Datasets:
| # 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. | |
| """ | |
| Load the Penn Treebank dataset. | |
| This is the Penn Treebank Project: Release 2 CDROM, featuring a million words of 1989 Wall | |
| Street Journal material. | |
| """ | |
| import datasets | |
| # TODO: Add BibTeX citation | |
| # Find for instance the citation on arxiv or on the dataset repo/website | |
| _CITATION = """\ | |
| @article{marcus-etal-1993-building, | |
| title = "Building a Large Annotated Corpus of {E}nglish: The {P}enn {T}reebank", | |
| author = "Marcus, Mitchell P. and | |
| Santorini, Beatrice and | |
| Marcinkiewicz, Mary Ann", | |
| journal = "Computational Linguistics", | |
| volume = "19", | |
| number = "2", | |
| year = "1993", | |
| url = "https://www.aclweb.org/anthology/J93-2004", | |
| pages = "313--330", | |
| } | |
| """ | |
| # TODO: Add description of the dataset here | |
| # You can copy an official description | |
| _DESCRIPTION = """\ | |
| This is the Penn Treebank Project: Release 2 CDROM, featuring a million words of 1989 Wall Street Journal material. This corpus has been annotated for part-of-speech (POS) information. In addition, over half of it has been annotated for skeletal syntactic structure. | |
| """ | |
| # TODO: Add a link to an official homepage for the dataset here | |
| _HOMEPAGE = "https://catalog.ldc.upenn.edu/LDC99T42" | |
| # TODO: Add the licence for the dataset here if you can find it | |
| _LICENSE = "LDC User Agreement for Non-Members" | |
| # TODO: Add link to the official dataset URLs here | |
| # 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://raw.githubusercontent.com/wojzaremba/lstm/master/data/" | |
| _TRAINING_FILE = "ptb.train.txt" | |
| _DEV_FILE = "ptb.valid.txt" | |
| _TEST_FILE = "ptb.test.txt" | |
| class PtbTextOnlyConfig(datasets.BuilderConfig): | |
| """BuilderConfig for PtbTextOnly""" | |
| def __init__(self, **kwargs): | |
| """BuilderConfig PtbTextOnly. | |
| Args: | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| super(PtbTextOnlyConfig, self).__init__(**kwargs) | |
| class PtbTextOnly(datasets.GeneratorBasedBuilder): | |
| """Load the Penn Treebank dataset.""" | |
| VERSION = datasets.Version("1.1.0") | |
| # This is an example of a dataset with multiple configurations. | |
| # If you don't want/need to define several sub-sets in your dataset, | |
| # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. | |
| # If you need to make complex sub-parts in the datasets with configurable options | |
| # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig | |
| # BUILDER_CONFIG_CLASS = MyBuilderConfig | |
| # You will be able to load one or the other configurations in the following list with | |
| # data = datasets.load_dataset('my_dataset', 'first_domain') | |
| # data = datasets.load_dataset('my_dataset', 'second_domain') | |
| BUILDER_CONFIGS = [ | |
| PtbTextOnlyConfig( | |
| name="penn_treebank", | |
| version=VERSION, | |
| description="Load the Penn Treebank dataset", | |
| ), | |
| ] | |
| def _info(self): | |
| features = datasets.Features({"sentence": datasets.Value("string")}) | |
| return datasets.DatasetInfo( | |
| # This is the description that will appear on the datasets page. | |
| description=_DESCRIPTION, | |
| # This defines the different columns of the dataset and their types | |
| features=features, # Here we define them above because they are different between the two configurations | |
| # If there's a common (input, target) tuple from the features, | |
| # specify them here. They'll be used if as_supervised=True in | |
| # builder.as_dataset. | |
| supervised_keys=None, | |
| # Homepage of the dataset for documentation | |
| homepage=_HOMEPAGE, | |
| # License for the dataset if available | |
| license=_LICENSE, | |
| # Citation for the dataset | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| """Returns SplitGenerators.""" | |
| # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration | |
| # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name | |
| # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs | |
| # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. | |
| # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive | |
| my_urls = { | |
| "train": f"{_URL}{_TRAINING_FILE}", | |
| "dev": f"{_URL}{_DEV_FILE}", | |
| "test": f"{_URL}{_TEST_FILE}", | |
| } | |
| data_dir = dl_manager.download_and_extract(my_urls) | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_dir["train"]}), | |
| datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_dir["test"]}), | |
| datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_dir["dev"]}), | |
| ] | |
| def _generate_examples(self, filepath): | |
| """Yields examples.""" | |
| # TODO: This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method. | |
| # It is in charge of opening the given file and yielding (key, example) tuples from the dataset | |
| # The key is not important, it's more here for legacy reason (legacy from tfds) | |
| with open(filepath, encoding="utf-8") as f: | |
| for id_, line in enumerate(f): | |
| line = line.strip() | |
| yield id_, {"sentence": line} | |