Datasets:

indonesian-nlp
/

mc4-id

	# 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 Indonesian split of the mC4 corpus."""
	import json
	import gzip
	import textwrap
	import datasets
	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 Italian 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"
	_BASE_URL = "https://huggingface.co/datasets/munggok/mc4-id/resolve/main/mc4-id-filter/c4-id{split_suffix}.tfrecord-{index:05d}-of-{n_shards:05d}.json.gz"
	_CONFIGS = {
	"tiny": {"train": 100, "validation": 1},
	"small": {"train": 250, "validation": 2},
	"medium": {"train": 500, "validation": 4},
	"large": {"train": 750, "validation": 6},
	"full": {"train": 1016, "validation": 8}
	}
	class CleanMc4IdConfig(datasets.BuilderConfig):
	"""BuilderConfig for the Clean mC4 Italian."""
	def __init__(self, **kwargs):
	"""BuilderConfig for Clean mC4 Italian.
	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 = [
	CleanMc4IdConfig(
	name="tiny",
	version=datasets.Version("1.0.0"),
	description=textwrap.dedent(
	f"""\
	A tiny cleaned version of the Italian portion of the multilingual C4 corpus.
	Estimated size of compressed files: 10GB
	"""
	)
	),
	CleanMc4IdConfig(
	name="small",
	version=datasets.Version("1.0.0"),
	description=textwrap.dedent(
	f"""\
	A small cleaned version of the Italian portion of the multilingual C4 corpus.
	Estimated size of compressed files: 25GB
	"""
	)
	),
	CleanMc4IdConfig(
	name="medium",
	version=datasets.Version("1.0.0"),
	description=textwrap.dedent(
	f"""\
	A medium cleaned version of the Italian portion of the multilingual C4 corpus.
	Estimated size of compressed files: 50GB
	"""
	)
	),
	CleanMc4IdConfig(
	name="large",
	version=datasets.Version("1.0.0"),
	description=textwrap.dedent(
	f"""\
	A large cleaned version of the Italian portion of the multilingual C4 corpus.
	Estimated size of compressed files: 75GB
	"""
	)
	),
	CleanMc4IdConfig(
	name="full",
	version=datasets.Version("1.0.0"),
	description=textwrap.dedent(
	f"""\
	The full cleaned version of the Italian portion of the multilingual C4 corpus.
	Estimated size of compressed files: 103GB
	"""
	)
	)
	]
	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 = {}
	for split in ["train", "validation"]:
	data_urls[split] = [
	_BASE_URL.format(
	split_suffix="-validation" if split == "validation" else "",
	index=index,
	n_shards=8 if split == "validation" else 1024,
	)
	for index in range(_CONFIGS[self.config.name][split])
	]
	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}
	),
	]
	def _generate_examples(self, filepaths):
	"""This function returns the examples in the raw (text) form by iterating on all the files."""
	id_ = 0
	for filepath in filepaths:
	logger.info(f"Generating examples from {filepath}")
	with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
	for line in f:
	if line:
	example = json.loads(line)
	yield id_, example
	id_ += 1