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# 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 |