# Datasets: asi /wikitext_fr

 # coding=utf-8 # Copyright 2020 The HuggingFace Datasets Authors and Antoine SIMOULIN. # # 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. """Wikitext-fr language modeling dataset consists of over 70 million tokens extracted from the set of french Wikipedia articles that are classified as "quality articles" or "good articles.". The aim is to replicate the English benchmark.""" import csv import json import os import datasets # TODO: Add BibTeX citation # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = """\ @inproceedings{simoulin:hal-03265900, TITLE = {{Un mod{\e}le Transformer G{\'e}n{\'e}ratif Pr{\'e}-entrain{\'e} pour le \_\_\_\_\_\_ fran{\c c}ais}}, AUTHOR = {Simoulin, Antoine and Crabb{\'e}, Benoit}, URL = {https://hal.archives-ouvertes.fr/hal-03265900}, BOOKTITLE = {{Traitement Automatique des Langues Naturelles}}, ADDRESS = {Lille, France}, EDITOR = {Denis, Pascal and Grabar, Natalia and Fraisse, Amel and Cardon, R{\'e}mi and Jacquemin, Bernard and Kergosien, Eric and Balvet, Antonio}, PUBLISHER = {{ATALA}}, PAGES = {246-255}, YEAR = {2021}, KEYWORDS = {fran{\c c}ais. ; GPT ; G{\'e}n{\'e}ratif ; Transformer ; Pr{\'e}-entra{\^i}n{\'e}}, PDF = {https://hal.archives-ouvertes.fr/hal-03265900/file/7.pdf}, HAL_ID = {hal-03265900}, HAL_VERSION = {v1}, } """ # TODO: Add description of the dataset here # You can copy an official description _DESCRIPTION = """\ Wikitext-fr language modeling dataset consists of over 70 million tokens extracted from the set of french Wikipedia articles that are classified as "quality articles" or "good articles.". The aim is to replicate the English benchmark.""" # TODO: Add a link to an official homepage for the dataset here _HOMEPAGE = "https://github.com/AntoineSimoulin/gpt-fr" # TODO: Add the licence for the dataset here if you can find it _LICENSE = "Creative Commons Attribution-ShareAlike License." # 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) # _URLs = { # 'wikitext-35': "./wikitext_35/", # 'wikitext-72': "./wikitext_72/", # } _URLs = { 'wikitext-35': "wikitext_35/wiki.zip", 'wikitext-72': "wikitext_72/wiki.zip", } # TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case class NewDataset(datasets.GeneratorBasedBuilder): """Wikitext-fr language modeling dataset consists of over 70 million tokens extracted from the set of french Wikipedia articles that are classified as "quality articles" or "good articles.". The aim is to replicate the English benchmark. """ 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 = [ datasets.BuilderConfig(name="wikitext-35", version=VERSION, description="This part covers quality articles only"), datasets.BuilderConfig(name="wikitext-72", version=VERSION, description="This part covers quality articles and good articles"), ] DEFAULT_CONFIG_NAME = "wikitext-35" # It's not mandatory to have a default configuration. Just use one if it make sense. def _info(self): features = datasets.Features({"paragraph": 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 = _URLs[self.config.name] data_dir = dl_manager.download_and_extract(my_urls) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data_dir, "wiki.train.tokens"), "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data_dir, "wiki.test.tokens"), "split": "test" }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data_dir, "wiki.valid.tokens"), "split": "dev", }, ), ] def _generate_examples( self, filepath, split # method parameters are unpacked from gen_kwargs as given in _split_generators ): """ Yields examples as (key, example) tuples. """ # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. # The key` is here for legacy reason (tfds) and is not important in itself. with open(filepath, 'r') as f: data = f.readlines() for id_, paragraph in enumerate(data): yield id_, {"paragraph": paragraph, }