Datasets:

german-nlp-group
/

german_common_crawl

Languages:

German

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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 csv
	import json
	import os
	import gzip

	import datasets


	# 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
	"""


	_URL = ["https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/german-nlp-group/german_common_crawl/de_head_0000_2015-48.tar.gz"]


	class GermanCommonCrawl(datasets.GeneratorBasedBuilder):
	"""TODO: Short description of my dataset."""

	VERSION = datasets.Version("1.1.0")


	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="first_part", version=VERSION, description="Download only one part (2 GB) instead of everythong (200 GB)"),
	datasets.BuilderConfig(name="data_only", version=VERSION, description="Only the website text without metadata"),
	datasets.BuilderConfig(name="metadata", version=VERSION, description="Metadata and raw text"),
	]

	DEFAULT_CONFIG_NAME = "metadata"

	def _info(self):
	if self.config.name == "data_only": # This is the name of the configuration selected in BUILDER_CONFIGS above
	features = datasets.Features(
	{
	"raw_content": datasets.Value("string"),
	}
	)
	else: # This is an example to show how to have different features for "first_domain" and "second_domain"
	features = datasets.Features(
	{
	"text": datasets.Value("string"),
	"url": 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"),
	"perplexity": datasets.Value("int32"),
	"bucket": datasets.Value("int32"),

	}
	)
	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, target) 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 == "first_part":
	data_dir = dl_manager.download_and_extract(_URL[0])

	else:
	data_dir = dl_manager.download_and_extract(_URL)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"folderpath": data_dir,
	"split": "train",
	},
	),
	]

	def _generate_examples(self, folderpath, split):
	""" Yields examples. """

	files = os.listdir(folderpath)

	if self.config == "first_part":
	files = os.path.join(folderpath, files[0])
	else:
	files = [os.path.join(folderpath, file) for file in files]

	#filepath = "/media/data/48_BERT/22_HF_Dataset/Data/de_head_0000_2015-48.tar.gz"

	for filepath in files:
	with gzip.open(filepath, 'rt', encoding="utf-8") as f:
	for id_, row in enumerate(f):
	data = eval(row)
	if self.config.name == "data_only":
	yield id_, {
	"raw_content": data["raw_content"],
	}
	else:
	yield id_, data