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german_common_crawl / german_common_crawl.py
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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
"""German Common Crawl"""
from __future__ import absolute_import, division, print_function
import datasets
import gzip
from ast import literal_eval
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@inproceedings{wenzek2020ccnet,
title={CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data},
author={Wenzek, Guillaume and Lachaux, Marie-Anne and Conneau, Alexis and Chaudhary, Vishrav and Guzm{\'a}n, Francisco and Joulin, Armand and Grave, {\'E}douard},
booktitle={Proceedings of The 12th Language Resources and Evaluation Conference},
pages={4003--4012},
year={2020}
}
"""
_DESCRIPTION = """\
German Only Extract from Common Crawl
This Dataset is for pretraining a German Language Model (Unsupervised) or tune a Multilingual Model specifically to German
"""
REPO_URL = "https://huggingface.co/datasets/flax-community/german_common_crawl/resolve/main/"
_URL_FIRST = [REPO_URL + file_name for file_name in [
"de_head_0000_2016-44.jsonl",
# "dummy.txt.gz",
]]
#TODO convert & upload all those files correctly
_URL_HEAD = [REPO_URL + file_name for file_name in [
"de_head_0000_2015-48.txt.gz",
"de_head_0000_2016-18.txt.gz",
"de_head_0000_2016-44.txt.gz",
"de_head_0000_2017-13.txt.gz",
"de_head_0000_2017-30.txt.gz",
"de_head_0000_2017-39.txt.gz",
"de_head_0000_2017-51.txt.gz",
"de_head_0000_2018-09.txt.gz",
"de_head_0000_2018-17.txt.gz",
"de_head_0000_2018-30.txt.gz",
"de_head_0000_2018-39.txt.gz",
"de_head_0000_2018-51.txt.gz",
"de_head_0000_2019-18.txt.gz",
"de_head_0000_2019-30.txt.gz",
"de_head_0000_2019-47.txt.gz",
"de_head_0000_2020-10.txt.gz",
"de_head_0001_2016-44.txt.gz",
"de_head_0001_2017-13.txt.gz",
"de_head_0001_2017-30.txt.gz",
"de_head_0001_2017-39.txt.gz",
"de_head_0001_2017-51.txt.gz",
"de_head_0001_2018-09.txt.gz",
"de_head_0001_2018-17.txt.gz",
"de_head_0001_2018-30.txt.gz",
"de_head_0001_2018-39.txt.gz",
"de_head_0001_2018-51.txt.gz",
"de_head_0001_2019-09.txt.gz",
"de_head_0001_2019-18.txt.gz",
"de_head_0001_2019-30.txt.gz",
"de_head_0001_2019-47.txt.gz",
"de_head_0001_2020-10.txt.gz",
"de_head_0002_2016-44.txt.gz",
"de_head_0002_2017-13.txt.gz",
"de_head_0002_2017-30.txt.gz",
"de_head_0002_2017-39.txt.gz",
"de_head_0002_2017-51.txt.gz",
"de_head_0002_2018-09.txt.gz",
"de_head_0002_2018-17.txt.gz",
"de_head_0002_2018-30.txt.gz",
"de_head_0002_2018-39.txt.gz",
"de_head_0002_2018-51.txt.gz",
"de_head_0002_2019-09.txt.gz",
"de_head_0002_2019-18.txt.gz",
"de_head_0002_2019-30.txt.gz",
"de_head_0002_2019-47.txt.gz",
"de_head_0002_2020-10.txt.gz",
"de_head_0003_2016-44.txt.gz",
"de_head_0003_2017-13.txt.gz",
"de_head_0003_2017-30.txt.gz",
"de_head_0003_2017-39.txt.gz",
"de_head_0003_2017-51.txt.gz",
"de_head_0003_2018-09.txt.gz",
"de_head_0003_2018-17.txt.gz",
"de_head_0003_2018-30.txt.gz",
"de_head_0003_2018-39.txt.gz",
"de_head_0003_2018-51.txt.gz",
"de_head_0003_2019-09.txt.gz",
"de_head_0003_2019-18.txt.gz",
"de_head_0003_2019-30.txt.gz",
"de_head_0003_2019-47.txt.gz",
"de_head_0003_2020-10.txt.gz",
"de_head_0004_2016-44.txt.gz",
"de_head_0004_2017-30.txt.gz",
"de_head_0004_2017-39.txt.gz",
"de_head_0004_2017-51.txt.gz",
"de_head_0004_2018-09.txt.gz",
"de_head_0004_2018-17.txt.gz",
"de_head_0004_2018-30.txt.gz",
"de_head_0004_2018-39.txt.gz",
"de_head_0004_2018-51.txt.gz",
"de_head_0004_2019-09.txt.gz",
"de_head_0004_2019-18.txt.gz",
"de_head_0004_2019-30.txt.gz",
"de_head_0004_2019-47.txt.gz",
"de_head_0004_2020-10.txt.gz",
"de_head_0005_2017-51.txt.gz",
"de_head_0005_2018-09.txt.gz",
"de_head_0005_2018-17.txt.gz",
"de_head_0005_2018-30.txt.gz",
"de_head_0005_2018-39.txt.gz",
"de_head_0005_2018-51.txt.gz",
"de_head_0005_2019-09.txt.gz",
"de_head_0005_2019-18.txt.gz",
"de_head_0005_2019-30.txt.gz",
"de_head_0005_2019-47.txt.gz",
"de_head_0005_2020-10.txt.gz",
"de_head_0006_2018-09.txt.gz",
"de_head_0006_2018-17.txt.gz",
"de_head_0006_2018-30.txt.gz",
"de_head_0006_2018-39.txt.gz",
"de_head_0006_2018-51.txt.gz",
"de_head_0006_2019-09.txt.gz",
"de_head_0006_2019-18.txt.gz",
"de_head_0006_2019-30.txt.gz",
"de_head_0006_2019-47.txt.gz",
"de_head_0006_2020-10.txt.gz",
"de_head_0007_2018-30.txt.gz",
"de_head_0007_2018-51.txt.gz",
"de_head_0007_2019-09.txt.gz",
"de_head_0007_2019-18.txt.gz",
"de_head_0007_2019-47.txt.gz",
"de_head_0007_2020-10.txt.gz",
]]
# TOOD add file names and convert and upload all of them
_URL_MIDDLE = [REPO_URL + file_name for file_name in [
]]
class GermanCommonCrawl(datasets.GeneratorBasedBuilder):
"""TODO: Short description of my dataset."""
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="first", version=VERSION, description="Only the first data file"),
datasets.BuilderConfig(name="head", version=VERSION, description=""), #TODO fill description
datasets.BuilderConfig(name="middle", version=VERSION, description=""), #TODO fill description
datasets.BuilderConfig(name="all", version=VERSION, description=""), #TODO fill description
]
def _info(self):
features = datasets.Features(
{
"url": datasets.Value("string"),
"date_download": datasets.Value("string"),
"digest": datasets.Value("string"),
"length": datasets.Value("int32"),
"nlines": datasets.Value("int32"),
"source_domain": datasets.Value("string"),
"title": datasets.Value("string"),
"raw_content": datasets.Value("string"),
"cc_segment": datasets.Value("string"),
"original_nlines": datasets.Value("int32"),
"original_length": datasets.Value("int32"),
"language": datasets.Value("string"),
"language_score": datasets.Value("int32"),
"perplexity": datasets.Value("int32"),
"bucket": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
# If there's a common (input, txtget) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
if self.config.name == "first":
data_files = dl_manager.download(_URL_FIRST)
elif self.config.name == "head":
data_files = dl_manager.download(_URL_HEAD)
elif self.config.name == "middle":
data_files = dl_manager.download(_URL_MIDDLE)
else:
data_files = dl_manager.download(_URL_HEAD + _URL_MIDDLE)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_files": data_files,
},
),
]
def _generate_examples(self, data_files):
"""This function returns the examples in the raw (text) form by iterating on all the files."""
for filepath in data_files:
with open(filepath, "rt", encoding="utf-8") as f:
# with gzip.open(filepath, "rt", encoding="utf-8") as f:
for id_, line in enumerate(f):
item = literal_eval(line)
yield id_, {
"url": item["url"],
"date_download": item["date_download"],
"digest": item["digest"],
"length": item["length"],
"nlines": item["nlines"],
"source_domain": item["source_domain"],
"title": item["title"],
"raw_content": item["raw_content"],
"cc_segment": item["cc_segment"],
"original_nlines": item["original_nlines"],
"original_length": item["original_length"],
"language": item["language"],
"language_score": item["language_score"],
"perplexity": item["perplexity"],
"bucket": item["bucket"],
}