Datasets:

gcaillaut
/

frwiki_good_pages_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 pandas as pd
	import re
	import gzip
	import json
	import datasets
	from pathlib import Path


	def get_open_method(path):
	path = Path(path)
	ext = path.suffix

	if ext == ".gz":
	import gzip
	open_func = gzip.open
	elif ext == ".bz2":
	import bz2
	open_func = bz2.open
	else:
	open_func = open
	return open_func


	def read_file(path):
	open_func = get_open_method(path)
	with open_func(path, "rt", encoding="UTF-8") as f:
	return f.read()


	# 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_good_pages_el"

	# TODO: Add the licence for the dataset here if you can find it
	_LICENSE = ""

	_URLs = {
	"frwiki": "data.tar.gz",
	"entities": "data.tar.gz",
	}

	_NER_CLASS_LABELS = [
	"B",
	"I",
	"O",
	]

	_ENTITY_TYPES = [
	"DATE",
	"PERSON",
	"GEOLOC",
	"ORG",
	"OTHER",
	]


	def text_to_el_features(doc_qid, doc_title, text, title2qid, title2wikipedia, title2wikidata):
	res = {
	"title": doc_title.replace("_", " "),
	"qid": doc_qid,
	}
	text_dict = {
	"words": [],
	"labels": [],
	"qids": [],
	"titles": [],
	"wikipedia": [],
	"wikidata": [],
	}
	entity_pattern = r"\[E=(.+?)\](.+?)\[/E\]"

	# start index of the previous text
	i = 0
	for m in re.finditer(entity_pattern, text):
	mention_title = m.group(1)
	mention = m.group(2)

	mention_qid = title2qid.get(mention_title, "").replace("_", " ")
	mention_wikipedia = title2wikipedia.get(mention_title, "")
	mention_wikidata = title2wikidata.get(mention_title, "")

	# Removes entity tags in descriptions
	mention_wikipedia = re.sub(entity_pattern, r"\2", mention_wikipedia)
	# Should not be necessary
	mention_wikidata = re.sub(entity_pattern, r"\2", mention_wikidata)

	# mention_qid = title2qid.get(mention_title, "YARIEN")
	# mention_wikipedia = title2wikipedia.get(mention_title, "YARIEN")
	# mention_wikidata = title2wikidata.get(mention_title, "YARIEN")

	mention_words = mention.split()

	j = m.start(0)
	prev_text = text[i:j].split()
	len_prev_text = len(prev_text)
	text_dict["words"].extend(prev_text)
	text_dict["labels"].extend(["O"] * len_prev_text)
	text_dict["qids"].extend([None] * len_prev_text)
	text_dict["titles"].extend([None] * len_prev_text)
	text_dict["wikipedia"].extend([None] * len_prev_text)
	text_dict["wikidata"].extend([None] * len_prev_text)

	text_dict["words"].extend(mention_words)

	# If there is no description, learning can’t be done so we treat the mention as not en entity
	if mention_wikipedia == "":
	len_mention = len(mention_words)
	text_dict["labels"].extend(["O"] * len_mention)
	text_dict["qids"].extend([None] * len_mention)
	text_dict["titles"].extend([None] * len_mention)
	text_dict["wikipedia"].extend([None] * len_mention)
	text_dict["wikidata"].extend([None] * len_mention)
	else:
	len_mention_tail = len(mention_words) - 1
	# wikipedia_words = mention_wikipedia.split()
	# wikidata_words = mention_wikidata.split()
	# title_words = mention_title.replace("_", " ").split()

	text_dict["labels"].extend(["B"] + ["I"] * len_mention_tail)
	text_dict["qids"].extend([mention_qid] + [None] * len_mention_tail)
	text_dict["titles"].extend(
	[mention_title] + [None] * len_mention_tail)
	text_dict["wikipedia"].extend(
	[mention_wikipedia] + [None] * len_mention_tail)
	text_dict["wikidata"].extend(
	[mention_wikidata] + [None] * len_mention_tail)

	i = m.end(0)

	tail = text[i:].split()
	len_tail = len(tail)
	text_dict["words"].extend(tail)
	text_dict["labels"].extend(["O"] * len_tail)
	text_dict["qids"].extend([None] * len_tail)
	text_dict["titles"].extend([None] * len_tail)
	text_dict["wikipedia"].extend([None] * len_tail)
	text_dict["wikidata"].extend([None] * len_tail)
	res.update(text_dict)
	return res


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

	VERSION = datasets.Version("0.1.0")

	# 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="frwiki", version=VERSION,
	description="The frwiki dataset for Entity Linking"),
	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 == "frwiki":
	features = datasets.Features({
	"title": datasets.Value("string"),
	"qid": datasets.Value("string"),
	"words": [datasets.Value("string")],
	"wikipedia": [datasets.Value("string")],
	"wikidata": [datasets.Value("string")],
	"labels": [datasets.ClassLabel(names=_NER_CLASS_LABELS)],
	"titles": [datasets.Value("string")],
	"qids": [datasets.Value("string")],
	})
	elif self.config.name == "entities":
	features = datasets.Features({
	"qid": datasets.Value("string"),
	"title": datasets.Value("string"),
	"url": datasets.Value("string"),
	"label": datasets.Value("string"),
	"aliases": [datasets.Value("string")],
	"type": datasets.ClassLabel(names=_ENTITY_TYPES),
	"wikipedia": datasets.Value("string"),
	"wikidata": 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

	# 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
	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={
	"data_dir": Path(data_dir, "data"),
	"split": "train"
	}
	)
	]

	def _generate_examples(
	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	self, data_dir, split
	):
	""" 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.

	entities_path = Path(data_dir, "entities.jsonl.gz")
	corpus_path = Path(data_dir, "corpus.jsonl.gz")

	def _identiy(x):
	return x

	if self.config.name == "frwiki":
	title2wikipedia = {}
	title2wikidata = {}
	title2qid = {}
	with gzip.open(entities_path, "rt", encoding="UTF-8") as ent_file:
	for line in ent_file:
	item = json.loads(
	line, parse_int=_identiy, parse_float=_identiy, parse_constant=_identiy)
	title = item["title"]
	title2wikipedia[title] = item["wikipedia_description"]
	title2wikidata[title] = item["wikidata_description"]
	title2qid[title] = item["qid"]

	with gzip.open(corpus_path, "rt", encoding="UTF-8") as crps_file:
	for id, line in enumerate(crps_file):
	item = json.loads(line, parse_int=lambda x: x,
	parse_float=lambda x: x, parse_constant=lambda x: x)
	qid = item["qid"]
	title = item["title"]
	text = item["text"]

	features = text_to_el_features(
	qid, title, text, title2qid, title2wikipedia, title2wikidata)
	yield id, features
	elif self.config.name == "entities":
	entity_pattern = r"\[E=(.+?)\](.+?)\[/E\]"
	with gzip.open(entities_path, "rt", encoding="UTF-8") as ent_file:
	for id, line in enumerate(ent_file):
	item = json.loads(
	line, parse_int=_identiy, parse_float=_identiy, parse_constant=_identiy)
	try:
	qid = item["qid"]
	item["wikipedia"] = re.sub(
	entity_pattern,
	r"\2",
	item.pop("wikipedia_description")
	)
	item["wikidata"] = item.pop("wikidata_description")
	if qid is None or qid == "":
	item["qid"] = ""
	item["wikidata"] = ""
	item["label"] = ""
	item["aliases"] = []
	if item["type"] not in _ENTITY_TYPES:
	item["type"] = "OTHER"
	yield id, item
	except:
	import sys
	print(item, file=sys.stderr)
	return