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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 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
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