Datasets:
c4

	"""C4 dataset based on Common Crawl."""


	import gzip
	import json

	import datasets


	logger = datasets.logging.get_logger(__name__)


	_DESCRIPTION = """\
	A colossal, cleaned version of Common Crawl's web crawl corpus.

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

	This is the processed version of Google's C4 dataset by AllenAI.
	"""

	_CITATION = """
	@article{2019t5,
	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 = {arXiv e-prints},
	year = {2019},
	archivePrefix = {arXiv},
	eprint = {1910.10683},
	}
	"""

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

	_VARIANTS = ["en", "realnewslike", "en.noblocklist", "en.noclean"]

	_N_SHARDS_PER_SPLIT = {
	"en": {"train": 1024, "validation": 8},
	"realnewslike": {"train": 512, "validation": 1},
	"en.noblocklist": {"train": 1024, "validation": 8},
	"en.noclean": {"train": 7168, "validation": 64},
	}

	_DATA_URL = "https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/{name}/c4-{split}.{index:05d}-of-{n_shards:05d}.json.gz"


	class C4(datasets.GeneratorBasedBuilder):
	"""C4, a colossal, cleaned version of Common Crawl's web crawl corpus."""

	BUILDER_CONFIGS = [datasets.BuilderConfig(name) for name in _VARIANTS]

	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=_URL,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	data_urls = {}
	for split in ["train", "validation"]:
	n_shards = _N_SHARDS_PER_SPLIT[self.config.name][split]
	data_urls[split] = [
	_DATA_URL.format(name=self.config.name, split=split, index=index, n_shards=n_shards)
	for index in range(n_shards)
	]
	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("generating examples from = %s", 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