Datasets:

yhavinga
/

mc4_nl_cleaned

	# 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.
	"""Cleaned Dutch split of the mC4 corpus."""


	import json
	import gzip
	import textwrap
	import datasets
	from itertools import zip_longest

	logger = datasets.logging.get_logger(__name__)

	_CITATION = """
	@article{JMLR:v21:20-074,
	author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
	title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
	journal = {Journal of Machine Learning Research},
	year = {2020},
	volume = {21},
	number = {140},
	pages = {1-67},
	url = {http://jmlr.org/papers/v21/20-074.html}
	}
	"""

	_DESCRIPTION = """\
	A thoroughly cleaned version of the Dutch portion of the multilingual
	colossal, cleaned version of Common Crawl's web crawl corpus (mC4) by AllenAI.

	Based on Common Crawl dataset: "https://commoncrawl.org".

	This is the processed version of Google's mC4 dataset by AllenAI, with further cleaning
	detailed in the repository README file.
	"""

	_HOMEPAGE = "https://github.com/allenai/allennlp/discussions/5056"

	_LICENSE = "Open Data Commons Attribution License (ODC-By) v1.0"

	_DATA_URL_NL = "https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned/resolve/main/mc4_nl_cleaned/{split}/c4-nl{validation}-cleaned.tfrecord-{index:05d}-of-{n_shards:05d}.json.gz"
	_DATA_URL_EN = "https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/{name}/c4-{split}.{index:05d}-of-{n_shards:05d}.json.gz"
	_C4_EN_VARIANT = "en"

	_CONFIG_NAMES = ["micro", "tiny", "small", "medium", "large", "full"]
	_CONFIG_EN_NL_SUFFIX = "_en_nl"

	_CONFIGS = dict(
	micro={"train": 2, "validation": 1, "estimate": "1GB"},
	tiny={"train": 100, "validation": 1, "estimate": "10GB"},
	small={"train": 250, "validation": 1, "estimate": "25GB"},
	medium={"train": 500, "validation": 2, "estimate": "50GB"},
	large={"train": 750, "validation": 3, "estimate": "75GB"},
	full={"train": 1024, "validation": 4, "estimate": "103GB"},
	)


	class Mc4NlCleanedConfig(datasets.BuilderConfig):
	"""BuilderConfig for mC4 NL Cleaned."""

	def __init__(self, **kwargs):
	"""BuilderConfig for mC4 NL Cleaned."
	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super().__init__(**kwargs)


	class Mc4(datasets.GeneratorBasedBuilder):
	"""mC4, a colossal, cleaned version of Common Crawl's web crawl corpus."""

	BUILDER_CONFIGS = [
	Mc4NlCleanedConfig(
	name=name,
	version=datasets.Version("1.0.0"),
	description=textwrap.dedent(
	f"""\
	A {name} cleaned version of the Dutch portion of the multilingual C4 corpus.
	Estimated size of compressed files: {_CONFIGS[name]['estimate']}
	"""
	),
	)
	for name in _CONFIG_NAMES
	]

	BUILDER_CONFIGS += [
	Mc4NlCleanedConfig(
	name=f"{name}{_CONFIG_EN_NL_SUFFIX}",
	version=datasets.Version("1.0.0"),
	description=textwrap.dedent(
	f"""\
	A {name} cleaned version of the Dutch and English portion of the multilingual C4 corpus.
	"""
	),
	)
	for name in _CONFIG_NAMES
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"text": datasets.Value("string"),
	"timestamp": datasets.Value("string"),
	"url": datasets.Value("string"),
	}
	),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	data_urls = {}
	config = _CONFIGS[self.config.name.replace(_CONFIG_EN_NL_SUFFIX, "")]
	for split in ["train", "validation"]:
	start_file = config.get("start", 0) if split == "train" else 0
	num_files = config.get(split)

	data_urls[split] = []
	for index in range(start_file, start_file + num_files):
	data_urls[split].append(
	_DATA_URL_NL.format(
	split=split,
	index=index,
	validation="-validation" if split == "validation" else "",
	n_shards=4 if split == "validation" else 1024,
	)
	)
	if self.config.name.endswith(_CONFIG_EN_NL_SUFFIX):
	data_urls[split].append(
	_DATA_URL_EN.format(
	name=_C4_EN_VARIANT,
	split=split,
	index=index,
	validation="-validation" if split == "validation" else "",
	n_shards=8 if split == "validation" else 1024,
	)
	)
	train_downloaded_files = dl_manager.download(data_urls["train"])
	validation_downloaded_files = dl_manager.download(data_urls["validation"])
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"filepaths": train_downloaded_files},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"filepaths": validation_downloaded_files},
	),
	]

	@staticmethod
	def grouper(iterable, n, fillvalue=None):
	"""Collect data into fixed-length chunks or blocks"""
	# grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx"
	args = [iter(iterable)] * n
	return zip_longest(*args, fillvalue=fillvalue)

	@staticmethod
	def gzip_open(filepath):
	if filepath:
	return gzip.open(open(filepath, "rb"), "rt", encoding="utf-8")

	def _generate_examples(self, filepaths):
	"""This function returns the examples in the raw (text) form by iterating on all the files."""
	id_ = 0
	for files in self.grouper(filepaths, 2, None):
	logger.info(f"Generating examples from {files}")
	gzip_iters = [self.gzip_open(file) for file in files if file is not None]
	for lines in zip(*gzip_iters):
	for line in lines:
	example = json.loads(line)
	yield id_, example
	id_ += 1