Datasets: huggingartists /bill-wurtz

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 9eddbe9                                                                                                                                                                                                                     123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107 # 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. """Lyrics dataset parsed from Genius""" import csv import json import os import gzip import datasets _CITATION = """\ @InProceedings{huggingartists:dataset, title = {Lyrics dataset}, author={Aleksey Korshuk }, year={2021} } """ _DESCRIPTION = """\ This dataset is designed to generate lyrics with HuggingArtists. """ # Add a link to an official homepage for the dataset here _HOMEPAGE = "https://github.com/AlekseyKorshuk/huggingartists" # Add the licence for the dataset here if you can find it _LICENSE = "All rights belong to copyright holders" _URL = "https://huggingface.co/datasets/huggingartists/bill-wurtz/resolve/main/datasets.json" # Name of the dataset class LyricsDataset(datasets.GeneratorBasedBuilder): """Lyrics dataset""" VERSION = datasets.Version("1.0.0") def _info(self): # This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset features = datasets.Features( { "text": 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.""" # 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 data_dir = dl_manager.download_and_extract(_URL) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": data_dir, "split": "train", }, ), ] def _generate_examples(self, filepath, split): """Yields examples as (key, example) tuples.""" # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. with open(filepath, encoding="utf-8") as f: data = json.load(f) for id, pred in enumerate(data[split]): yield id, {"text": pred}