Datasets:
Tasks:
Text Generation
Sub-tasks:
language-modeling
Languages:
Italian
Size:
100M<n<1B
ArXiv:
License:
# 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 Italian 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/gsarti/clean_mc4_it/resolve/main/clean-mc4-it/c4-it{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": 1024, "validation": 8} | |
} | |
class CleanMc4ItConfig(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 = [ | |
CleanMc4ItConfig( | |
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 | |
""" | |
) | |
), | |
CleanMc4ItConfig( | |
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 | |
""" | |
) | |
), | |
CleanMc4ItConfig( | |
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 | |
""" | |
) | |
), | |
CleanMc4ItConfig( | |
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 | |
""" | |
) | |
), | |
CleanMc4ItConfig( | |
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 | |