# 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}