Datasets:

gcaillaut
/

frwiki_el

	# 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 re
	import gzip
	import json
	import datasets
	from pathlib import Path


	# TODO: Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = ""

	_DESCRIPTION = """\
	French Wikipedia dataset for Entity Linking
	"""

	_HOMEPAGE = "https://github.com/GaaH/frwiki_el"

	_LICENSE = "WTFPL"

	_URLs = {
	"frwiki": "data/frwiki-20220901/corpus.jsonl.gz",
	"frwiki-mini": "data/frwiki-20220901/corpus_mini.jsonl.gz",
	"frwiki-abstracts": "data/frwiki-20220901/corpus_abstracts.jsonl.gz",
	"entities": "data/frwiki-20220901/entities.jsonl.gz",
	}

	class FrwikiElDataset(datasets.GeneratorBasedBuilder):
	"""
	"""

	VERSION = datasets.Version("0.2.3")

	# 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="frwiki", version=VERSION,
	description="The frwiki dataset for Entity Linking"),
	datasets.BuilderConfig(name="frwiki-mini", version=VERSION,
	description="1000 first sentences of the frwiki dataset for Entity Linking"),
	datasets.BuilderConfig(name="frwiki-abstracts", version=VERSION,
	description="Abstracts (first paragraph) of the frwiki pages."),
	datasets.BuilderConfig(name="entities", version=VERSION,
	description="Entities and their descriptions"),
	]

	# It's not mandatory to have a default configuration. Just use one if it make sense.
	DEFAULT_CONFIG_NAME = "frwiki"

	def _info(self):
	if self.config.name in ("frwiki", 'frwiki-mini', 'frwiki-abstracts'):
	features = datasets.Features({
	"name": datasets.Value("string"),
	"wikidata_id": datasets.Value("string"),
	"wikipedia_id": datasets.Value("int32"),
	"wikipedia_url": datasets.Value("string"),
	"wikidata_url": datasets.Value("string"),
	"sentences": [{
	"text": datasets.Value("string"),
	"ner": [datasets.Value("string")],
	"mention_mappings": [[datasets.Value("int16")]],
	"el_wikidata_id": [datasets.Value("string")],
	"el_wikipedia_id": [datasets.Value("int32")],
	"el_wikipedia_title": [datasets.Value("string")],
	}]
	})
	elif self.config.name == "entities":
	features = datasets.Features({
	"name": datasets.Value("string"),
	"wikidata_id": datasets.Value("string"),
	"wikipedia_id": datasets.Value("int32"),
	"wikipedia_url": datasets.Value("string"),
	"wikidata_url": datasets.Value("string"),
	"description": 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
	# Here we define them above because they are different between the two configurations
	features=features,
	# 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

	filepath = _URLs[self.config.name]
	path = dl_manager.download(filepath)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"path": path,
	}
	)
	]

	def _generate_examples(self, path):
	""" 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.

	# We need to use open before gzip.open in case the dataset is streamed
	# https://github.com/huggingface/datasets/issues/2607#issuecomment-883219727
	with gzip.open(open(path, 'rb'), "rt", encoding="UTF-8") as datafile:
	for id, line in enumerate(datafile):
	item = json.loads(line)
	yield id, item