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# 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)
)
|