2021_39/raw/2021_39_raw.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.
# 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
import zstandard as zstd
logger = datasets.logging.get_logger(__name__)

file = sorted(glob.glob('/data/KoPI-CC/2021_39/raw/*.zst'))
_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 zstd.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
                for line in f:
                    if line:
                        example = json.loads(line)
                        meta = dict()
                        meta["warc_headers"] = example["warc_headers"]
                        meta["warc_headers"]["warc-identified-content-language"] = example[
                            "warc_headers"
                        ].get("warc-identified-content-language")
                        meta["identification"] = example["metadata"]["identification"]
                        meta["annotations"] = example["metadata"]["annotation"]
                        meta["line_identifications"] = example["metadata"][
                            "sentence_identifications"
                        ]
                        yield id_, {'text':example['content'],'url':example['warc_headers']['warc-target-uri'],'timestamp':example['warc_headers']['warc-date'],"meta": json.dumps(meta)}
                        id_ += 1