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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
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