Datasets:
Languages:
English
Multilinguality:
multilingual
Size Categories:
100M<n<1B
Language Creators:
found
Annotations Creators:
no-annotation
Source Datasets:
original
ArXiv:
License:
"""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 | |