Datasets:

Jean-Baptiste
/

wikiner_fr

	# 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"]}