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# 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 glob
import gzip
import textwrap
import datasets
logger = datasets.logging.get_logger(__name__)
file = sorted(glob.glob('/data/oscar_2021-31_dedup/*.gz'))
_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 OscarConfig(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 Oscar(datasets.GeneratorBasedBuilder):
"""mC4, a colossal, cleaned version of Common Crawl's web crawl corpus."""
BUILDER_CONFIGS = [
OscarConfig(
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"),
"url": datasets.Value("string"),
"timestamp": datasets.Value("string"),
"meta": datasets.Value("string"),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data_urls = {}
train_downloaded_files = file
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": train_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_, {'text':example['text'],'url':example['url'],'timestamp':example['timestamp'],'meta': example['meta']}
id_ += 1
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