mc4_nl_cleaned.py

# 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.
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# limitations under the License.
"""Cleaned Dutch 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 Dutch 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/yhavinga/mc4_nl_cleaned/resolve/main/mc4_nl_cleaned/{split}/c4-nl{validation}-cleaned.tfrecord-{index:05d}-of-{n_shards:05d}.json.gz"

_CONFIGS = dict(
    tiny={"train": 100, "validation": 1},
    small={"train": 250, "validation": 1},
    medium={"train": 500, "validation": 2},
    large={"train": 750, "validation": 3},
    full={"train": 1024, "validation": 4},
)


class Mc4NlCleanedConfig(datasets.BuilderConfig):
    """BuilderConfig for mC4 NL Cleaned."""

    def __init__(self, **kwargs):
        """BuilderConfig for mC4 NL Cleaned."
        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 = [
        Mc4NlCleanedConfig(
            name="tiny",
            version=datasets.Version("1.0.0"),
            description=textwrap.dedent(
                f"""\
                A tiny cleaned version of the Dutch portion of the multilingual C4 corpus.
                Estimated size of compressed files: 10GB
                """
            ),
        ),
        Mc4NlCleanedConfig(
            name="small",
            version=datasets.Version("1.0.0"),
            description=textwrap.dedent(
                f"""\
                A small cleaned version of the Dutch portion of the multilingual C4 corpus.
                Estimated size of compressed files: 25GB
                """
            ),
        ),
        Mc4NlCleanedConfig(
            name="medium",
            version=datasets.Version("1.0.0"),
            description=textwrap.dedent(
                f"""\
                A medium cleaned version of the Dutch portion of the multilingual C4 corpus.
                Estimated size of compressed files: 50GB
                """
            ),
        ),
        Mc4NlCleanedConfig(
            name="large",
            version=datasets.Version("1.0.0"),
            description=textwrap.dedent(
                f"""\
                A large cleaned version of the Dutch portion of the multilingual C4 corpus.
                Estimated size of compressed files: 75GB
                """
            ),
        ),
        Mc4NlCleanedConfig(
            name="full",
            version=datasets.Version("1.0.0"),
            description=textwrap.dedent(
                f"""\
                The full cleaned version of the Dutch 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=split,
                    index=index,
                    validation="-validation" if split=="validation" else "",
                    n_shards=4 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