Datasets:
Languages:
English
Multilinguality:
multilingual
Size Categories:
100M<n<1B
Language Creators:
found
Annotations Creators:
no-annotation
Source Datasets:
original
ArXiv:
License:
File size: 3,294 Bytes
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"""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
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