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:
Tags:
License:
File size: 6,010 Bytes
ba1ca81 e9b0549 ba1ca81 aee85ce ba1ca81 4ad35b8 ba1ca81 e9b0549 ba1ca81 aee85ce ba1ca81 aee85ce ba1ca81 aee85ce ba1ca81 aee85ce |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 |
# 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,
}
|