Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Multilinguality:
multilingual
Size Categories:
unknown
Language Creators:
found
Annotations Creators:
machine-generated
Source Datasets:
original
ArXiv:
License:
# coding=utf-8 | |
# Copyright 2020 HuggingFace Datasets Authors. | |
# | |
# 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. | |
# Lint as: python3 | |
"""The Polyglot-NER Dataset.""" | |
import datasets | |
_CITATION = """\ | |
@article{polyglotner, | |
author = {Al-Rfou, Rami and Kulkarni, Vivek and Perozzi, Bryan and Skiena, Steven}, | |
title = {{Polyglot-NER}: Massive Multilingual Named Entity Recognition}, | |
journal = {{Proceedings of the 2015 {SIAM} International Conference on Data Mining, Vancouver, British Columbia, Canada, April 30- May 2, 2015}}, | |
month = {April}, | |
year = {2015}, | |
publisher = {SIAM}, | |
} | |
""" | |
_LANGUAGES = [ | |
"ca", | |
"de", | |
"es", | |
"fi", | |
"hi", | |
"id", | |
"ko", | |
"ms", | |
"pl", | |
"ru", | |
"sr", | |
"tl", | |
"vi", | |
"ar", | |
"cs", | |
"el", | |
"et", | |
"fr", | |
"hr", | |
"it", | |
"lt", | |
"nl", | |
"pt", | |
"sk", | |
"sv", | |
"tr", | |
"zh", | |
"bg", | |
"da", | |
"en", | |
"fa", | |
"he", | |
"hu", | |
"ja", | |
"lv", | |
"no", | |
"ro", | |
"sl", | |
"th", | |
"uk", | |
] | |
_DESCRIPTION = """\ | |
Polyglot-NER | |
A training dataset automatically generated from Wikipedia and Freebase the task | |
of named entity recognition. The dataset contains the basic Wikipedia based | |
training data for 40 languages we have (with coreference resolution) for the task of | |
named entity recognition. The details of the procedure of generating them is outlined in | |
Section 3 of the paper (https://arxiv.org/abs/1410.3791). Each config contains the data | |
corresponding to a different language. For example, "es" includes only spanish examples. | |
""" | |
_DATA_URL = "http://cs.stonybrook.edu/~polyglot/ner2/emnlp_datasets.tgz" | |
_HOMEPAGE_URL = "https://sites.google.com/site/rmyeid/projects/polylgot-ner" | |
_VERSION = "1.0.0" | |
_COMBINED = "combined" | |
class PolyglotNERConfig(datasets.BuilderConfig): | |
def __init__(self, *args, languages=None, **kwargs): | |
super().__init__(*args, version=datasets.Version(_VERSION, ""), **kwargs) | |
self.languages = languages | |
assert all(lang in _LANGUAGES for lang in languages), f"Invalid languages. Please use a subset of {_LANGUAGES}" | |
class PolyglotNER(datasets.GeneratorBasedBuilder): | |
"""The Polyglot-NER Dataset""" | |
BUILDER_CONFIGS = [ | |
PolyglotNERConfig(name=lang, languages=[lang], description=f"Polyglot-NER examples in {lang}.") | |
for lang in _LANGUAGES | |
] + [ | |
PolyglotNERConfig( | |
name=_COMBINED, languages=_LANGUAGES, description="Complete Polyglot-NER dataset with all languages." | |
) | |
] | |
DEFAULT_CONFIG_NAME = _COMBINED | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
{ | |
"id": datasets.Value("string"), | |
"lang": datasets.Value("string"), | |
"words": datasets.Sequence(datasets.Value("string")), | |
"ner": datasets.Sequence(datasets.Value("string")), | |
} | |
), | |
supervised_keys=None, | |
homepage=_HOMEPAGE_URL, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
"""Returns SplitGenerators.""" | |
archive = dl_manager.download(_DATA_URL) | |
return [ | |
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"files": dl_manager.iter_archive(archive)}) | |
] | |
def _generate_examples(self, files): | |
languages = list(self.config.languages) | |
sentence_counter = 0 | |
for path, f in files: | |
if not languages: | |
break | |
if path.endswith("_wiki.conll"): | |
lang = path.split("/")[1] | |
if lang in languages: | |
languages.remove(lang) | |
current_words = [] | |
current_ner = [] | |
for row in f: | |
row = row.decode("utf-8").rstrip() | |
if row: | |
token, label = row.split("\t") | |
current_words.append(token) | |
current_ner.append(label) | |
else: | |
# New sentence | |
if not current_words: | |
# Consecutive empty lines will cause empty sentences | |
continue | |
assert len(current_words) == len(current_ner), "💔 between len of words & ner" | |
sentence = ( | |
sentence_counter, | |
{ | |
"id": str(sentence_counter), | |
"lang": lang, | |
"words": current_words, | |
"ner": current_ner, | |
}, | |
) | |
sentence_counter += 1 | |
current_words = [] | |
current_ner = [] | |
yield sentence | |
# Don't forget last sentence in dataset 🧐 | |
if current_words: | |
yield sentence_counter, { | |
"id": str(sentence_counter), | |
"lang": lang, | |
"words": current_words, | |
"ner": current_ner, | |
} | |