Datasets:
File size: 5,185 Bytes
eab50b5 b534c8d eab50b5 dcb6591 eab50b5 f6e001e eab50b5 f6e001e eab50b5 f6e001e eab50b5 b534c8d eab50b5 |
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 The TensorFlow Datasets Authors and the 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.
"""Wiki40B: A clean Wikipedia dataset for 40+ languages."""
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """
"""
_DESCRIPTION = """
Clean-up text for 40+ Wikipedia languages editions of pages
correspond to entities. The datasets have train/dev/test splits per language.
The dataset is cleaned up by page filtering to remove disambiguation pages,
redirect pages, deleted pages, and non-entity pages. Each example contains the
wikidata id of the entity, and the full Wikipedia article after page processing
that removes non-content sections and structured objects.
"""
_LICENSE = """
This work is licensed under the Creative Commons Attribution-ShareAlike
3.0 Unported License. To view a copy of this license, visit
http://creativecommons.org/licenses/by-sa/3.0/ or send a letter to
Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
"""
_URL = "https://research.google/pubs/pub49029/"
_DATA_DIRECTORY = "gs://tfds-data/downloads/wiki40b/tfrecord_prod"
WIKIPEDIA_LANGUAGES = [
"en",
"ar",
"zh-cn",
"zh-tw",
"nl",
"fr",
"de",
"it",
"ja",
"ko",
"pl",
"pt",
"ru",
"es",
"th",
"tr",
"bg",
"ca",
"cs",
"da",
"el",
"et",
"fa",
"fi",
"he",
"hi",
"hr",
"hu",
"id",
"lt",
"lv",
"ms",
"no",
"ro",
"sk",
"sl",
"sr",
"sv",
"tl",
"uk",
"vi",
]
class Wiki40bConfig(datasets.BuilderConfig):
"""BuilderConfig for Wiki40B."""
def __init__(self, language=None, **kwargs):
"""BuilderConfig for Wiki40B.
Args:
language: string, the language code for the Wiki40B dataset to use.
**kwargs: keyword arguments forwarded to super.
"""
super(Wiki40bConfig, self).__init__(
name=str(language), description=f"Wiki40B dataset for {language}.", **kwargs
)
self.language = language
_VERSION = datasets.Version("1.1.0")
class Wiki40b(datasets.BeamBasedBuilder):
"""Wiki40B: A Clean Wikipedia Dataset for Mutlilingual Language Modeling."""
BUILDER_CONFIGS = [
Wiki40bConfig(
version=_VERSION,
language=lang,
) # pylint:disable=g-complex-comprehension
for lang in WIKIPEDIA_LANGUAGES
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"wikidata_id": datasets.Value("string"),
"text": datasets.Value("string"),
"version_id": datasets.Value("string"),
}
),
supervised_keys=None,
homepage=_URL,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
lang = self.config.language
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepaths": f"{_DATA_DIRECTORY}/train/{lang}_examples-*"},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepaths": f"{_DATA_DIRECTORY}/dev/{lang}_examples-*"},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepaths": f"{_DATA_DIRECTORY}/test/{lang}_examples-*"},
),
]
def _build_pcollection(self, pipeline, filepaths):
"""Build PCollection of examples."""
import apache_beam as beam
import tensorflow as tf
logger.info("generating examples from = %s", filepaths)
def _extract_content(example):
"""Extracts content from a TFExample."""
wikidata_id = example.features.feature["wikidata_id"].bytes_list.value[0].decode("utf-8")
text = example.features.feature["text"].bytes_list.value[0].decode("utf-8")
version_id = example.features.feature["version_id"].bytes_list.value[0].decode("utf-8")
# wikidata_id could be duplicated with different texts.
yield wikidata_id + text, {
"wikidata_id": wikidata_id,
"text": text,
"version_id": version_id,
}
return (
pipeline
| beam.io.ReadFromTFRecord(filepaths, coder=beam.coders.ProtoCoder(tf.train.Example))
| beam.FlatMap(_extract_content)
)
|