# 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. """Wikiner dataset for NER in french language """ from __future__ import absolute_import, division, print_function import csv import json import os import datasets from datasets import Dataset _NER_LABEL_NAMES = [ "O", "LOC", "PER", "MISC", "ORG" ] # TODO: Add BibTeX citation # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = """\ @InProceedings{huggingface:dataset, title = {Wikiner dataset for NER task in french}, authors={Created by Nothman et al. at 2013}, year={2013} } """ # TODO: Add description of the dataset here # You can copy an official description _DESCRIPTION = """\ Dataset can be used to train on NER task for french langugage. """ # TODO: Add a link to an official homepage for the dataset here _HOMEPAGE = "https://www.sciencedirect.com/science/article/pii/S0004370212000276?via%3Dihub" # TODO: Add the licence for the dataset here if you can find it _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 = { "NER": "https://huggingface.co/datasets/Jean-Baptiste/wikiner_fr/resolve/main/data.zip" } # TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case class WikinerFr(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("1.0.0") # 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="NER", version="0.0.1", description="Dataset for entity recognition") ] DEFAULT_CONFIG_NAME = "NER" # 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 == "NER": # This is the name of the configuration selected in BUILDER_CONFIGS above features = datasets.Features( { "id": datasets.Value("int32"), "ner_tags": datasets.Sequence( feature=datasets.ClassLabel(num_classes=len(_NER_LABEL_NAMES), names=_NER_LABEL_NAMES) ), "tokens": datasets.Sequence(feature=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=("id", "ner_tags"), # 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 if self.config.data_dir: data_dir = self.config.data_dir else: 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, "data","train"), "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data_dir, "data", "test"), "split": "test" }, ) ] 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. dataset = Dataset.load_from_disk(filepath) for i in range(0, len(dataset)): # print(dataset[i]) yield i, {"id": str(dataset[i]["id"]),"tokens": dataset[i]["tokens"], "ner_tags": dataset[i]["ner_tags"]}