Datasets:
Multilinguality:
multilingual
Language Creators:
crowdsourced
Annotations Creators:
no-annotation
Source Datasets:
original
Tags:
License:
File size: 4,808 Bytes
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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.
# Lint as: python3
"""Wikipedia dataset containing cleaned articles of all languages."""
import json
import datasets
import pyarrow.parquet as pq
from .lang_def import WIKIPEDIA_LANGUAGES
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@ONLINE {wikidump,
author = {Wikimedia Foundation},
title = {Wikimedia Downloads},
url = {https://dumps.wikimedia.org}
}
"""
_DESCRIPTION = """\
Wikipedia dataset containing cleaned articles of all languages.
The datasets are built from the Wikipedia dump
(https://dumps.wikimedia.org/) with one split per language. Each example
contains the content of one full Wikipedia article with cleaning to strip
markdown and unwanted sections (references, etc.).
"""
_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."
)
_BASE_URL_TMPL = "data/{date}/{lang}"
_VERSION = datasets.Version("1.2.0", "")
class WikipediaConfig(datasets.BuilderConfig):
"""BuilderConfig for Wikipedia."""
def __init__(self, language=None, date=None, version=_VERSION, **kwargs):
"""BuilderConfig for Wikipedia.
Args:
language: string, the language code for the Wikipedia dump to use.
date: string, date of the Wikipedia dump in YYYYMMDD format. A list of
available dates can be found at https://dumps.wikimedia.org/enwiki/.
**kwargs: keyword arguments forwarded to super.
"""
super().__init__(
name=f"{date}.{language}",
description=f"Wikipedia dataset for {language}, parsed from {date} dump.",
version=version,
**kwargs,
)
self.date = date
self.language = language
class Wikipedia(datasets.ArrowBasedBuilder):
"""Wikipedia dataset."""
# Use mirror (your.org) to avoid download caps.
BUILDER_CONFIG_CLASS = WikipediaConfig
BUILDER_CONFIGS = [
WikipediaConfig(
language=lang,
date=date,
) # pylint:disable=g-complex-comprehension
for date in WIKIPEDIA_LANGUAGES.keys()
for lang in WIKIPEDIA_LANGUAGES[date]
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"url": datasets.Value("string"),
"title": datasets.Value("string"),
"text": datasets.Value("string"),
}
),
# No default supervised_keys.
supervised_keys=None,
homepage="https://dumps.wikimedia.org",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
def _base_url(lang):
return _BASE_URL_TMPL.format(lang=lang, date=self.config.date)
lang = self.config.language
info_file_url = _base_url(lang) + "/info.json"
downloaded_info_file = dl_manager.download_and_extract({"info": info_file_url})
with open(downloaded_info_file["info"], "r") as f:
info = json.load(f)
shard_urls = [
_base_url(lang) + "/" + shard_name for shard_name in info["shards"]
]
downloaded_files = dl_manager.download({"train": shard_urls})
logger.info("downloaded_files = %s", downloaded_files)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepaths": downloaded_files["train"]},
),
]
def _generate_tables(self, filepaths):
"""This function returns the examples in the raw (text) form."""
for filepath in filepaths:
with open(filepath, "rb") as f:
pf = pq.ParquetFile(f)
for group_i in range(pf.num_row_groups):
tbl = pf.read_row_group(group_i)
yield group_i, tbl
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