# 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: Address all TODOs and remove all explanatory comments """TODO: Add a description here.""" 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{sagot:inria-00521242, TITLE = {{The Lefff, a freely available and large-coverage morphological and syntactic lexicon for French}}, AUTHOR = {Sagot, Beno{\^i}t}, URL = {https://hal.inria.fr/inria-00521242}, BOOKTITLE = {{7th international conference on Language Resources and Evaluation (LREC 2010)}}, ADDRESS = {Valletta, Malta}, YEAR = {2010}, MONTH = May, PDF = {https://hal.inria.fr/inria-00521242/file/lrec10lefff.pdf}, HAL_ID = {inria-00521242}, HAL_VERSION = {v1}, }""" # TODO: Add description of the dataset here # You can copy an official description _DESCRIPTION = """\ The lefff-morpho dataset gives access to the morphological information, in both its original format and the UniMorph format. """ # TODO: Add a link to an official homepage for the dataset here _HOMEPAGE = "http://almanach.inria.fr/software_and_resources/custom/Alexina-en.html" # TODO: Add the licence for the dataset here if you can find it _LICENSE = "LGPL-LR" # TODO: Add link to the official dataset URLs here # The HuggingFace Datasets library doesn't host the datasets but only points to the original files. # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) _URLS = { "all": "lefff_morpho-3.5.json", } # TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case class LefffMorpho(datasets.GeneratorBasedBuilder): """The lefff-morpho dataset gives access to the morphological information, in both its original format and the UniMorph format.""" VERSION = datasets.Version("3.5.0") # 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') DEFAULT_CONFIG_NAME = "all" # 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 features = datasets.Features({ "form": datasets.Value("string"), "lemma": datasets.Value("string"), "category": datasets.Value("string"), "type": datasets.Value("string"), "msfeatures": datasets.Value("string"), "unimorph": datasets.Value("string"), "expansion": 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, uncomment supervised_keys line below and # specify them. They'll be used if as_supervised=True in builder.as_dataset. # supervised_keys=("sentence", "label"), # 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): # 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 urls = _URLS["all"] downloaded_files = dl_manager.download_and_extract(_URLS) return [datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={'filepath': downloaded_files['all']})] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, filepath): # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. with open(filepath, encoding="utf-8") as f: lefff_morpho = json.load(f) for key, row in enumerate(lefff_morpho): yield key, row