| # 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. | |
| """TODO: Add a description here.""" | |
| import csv | |
| import json | |
| import os | |
| import sys | |
| import datasets | |
| import yaml | |
| with open("config.yml", "r") as ymlfile: | |
| cfg = yaml.safe_load(ymlfile) | |
| print('description : ', cfg['citation']) | |
| # TODO: Add BibTeX citation | |
| # Find for instance the citation on arxiv or on the dataset repo/website | |
| _CITATION = cfg['citation'] | |
| # TODO: Add description of the dataset here | |
| # You can copy an official description | |
| _DESCRIPTION = cfg['desc'] | |
| # TODO: Add a link to an official homepage for the dataset here | |
| _HOMEPAGE = cfg['homepage'] | |
| # TODO: Add the licence for the dataset here if you can find it | |
| _LICENSE = cfg['licence'] | |
| # 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://drive.google.com/drive/folders/1Jvvddj0abp9rMAGhqXqpPbE8-Y28iyM3?usp=sharing" | |
| _URLs = { | |
| "train": cfg['data']['train'],#"train_.json",#"https://drive.google.com/uc?export=download&id=1-0r83eGwRfmfVAEtdyiregvneJ81ev1N", | |
| "validation": cfg['data']['validation'],#"validation_.json",#"https://drive.google.com/uc?export=download&id=1-0zTW12OKIPrH3Zmad6tvSte7bA8O4RR", | |
| "test": cfg['data']['test']#"test_.json"#"https://drive.google.com/uc?export=download&id=1-2U6BeDluJxIieMA6JTjLoij_KcMhl4Y" | |
| } | |
| # print(cfg['translation_fesat']) | |
| # TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case | |
| class NewDataset(datasets.GeneratorBasedBuilder): | |
| VERSION = datasets.Version(cfg['version']) | |
| # 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=cfg['dataset_name'], version=VERSION, description=cfg['desc']) | |
| ] | |
| # DEFAULT_CONFIG_NAME = "opus_books_es_it" # It's not mandatory to have a default configuration. Just use one if it make sense. | |
| def _info(self): | |
| # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset | |
| # if self.config.name == "opus_books_es_it": # This is the name of the configuration selected in BUILDER_CONFIGS above | |
| features=datasets.Features({"translation": datasets.features.Translation(languages=("Spanish", "Italian"))}) | |
| # features=datasets.Features({cfg["title"]: datasets.features.Translation(languages=(cfg["col1"], cfg{"col2"}))}) | |
| # features = datasets.Features( | |
| # { | |
| # "Spanish": datasets.Value("string"), | |
| # "Italian": datasets.Value("string") | |
| # # These are the features of your dataset like images, labels ... | |
| # } | |
| # ) | |
| 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 | |
| dl_path = dl_manager.download_and_extract(_URLs) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "filepath": dl_path['train'], | |
| "split": "train", | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "filepath": dl_path['test'], | |
| "split": "test" | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "filepath": dl_path['validation'], | |
| "split": "validation", | |
| }, | |
| ), | |
| ] | |
| 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, encoding="utf-8") as f: | |
| for id_, line in enumerate(f): | |
| data = json.loads(line) | |
| yield id_, { | |
| "translation":{"Spanish": data["Spanish"], | |
| "Italian": data["Italian"]#"" if split == "test" else data["Italian"], | |
| } | |
| } | |
| # ''' |