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# coding=utf-8
# Copyright 2022 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.
"""
KoPI-CC corpus
[seacrowd_schema_name] = ssp
"""
import gzip
import json
from typing import List
import datasets
import zstandard as zstd
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import (DEFAULT_SEACROWD_VIEW_NAME,
DEFAULT_SOURCE_VIEW_NAME, Tasks)
_DATASETNAME = "kopi_cc"
_LANGUAGES = ["ind"]
_LOCAL = False
_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
_UNIFIED_VIEW_NAME = DEFAULT_SEACROWD_VIEW_NAME
_URL = "https://commoncrawl.org/"
_CITATION = """\
@ARTICLE{2022arXiv220106642A,
author = {{Abadji}, Julien and {Ortiz Suarez}, Pedro and {Romary}, Laurent and {Sagot}, Benoit},
title = "{Towards a Cleaner Document-Oriented Multilingual Crawled Corpus}",
journal = {arXiv e-prints},
keywords = {Computer Science - Computation and Language},
year = 2022,
month = jan,
eid = {arXiv:2201.06642},
pages = {arXiv:2201.06642},
archivePrefix = {arXiv},
eprint = {2201.06642},
primaryClass = {cs.CL},
adsurl = {https://ui.adsabs.harvard.edu/abs/2022arXiv220106642A},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@inproceedings{AbadjiOrtizSuarezRomaryetal.2021,
author = {Julien Abadji and Pedro Javier Ortiz Su{\'a}rez and Laurent Romary and Benoit Sagot},
title = {Ungoliant: An optimized pipeline for the generation of a very large-scale multilingual web corpus},
series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-9) 2021. Limerick, 12 July 2021 (Online-Event)},
editor = {Harald L{\"u}ngen and Marc Kupietz and Piotr Bański and Adrien Barbaresi and Simon Clematide and Ines Pisetta},
publisher = {Leibniz-Institut f{\"u}r Deutsche Sprache},
address = {Mannheim},
doi = {10.14618/ids-pub-10468},
url = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-104688},
pages = {1 -- 9},
year = {2021},
abstract = {Since the introduction of large language models in Natural Language Processing, large raw corpora have played a crucial role in Computational Linguistics.},
language = {en}
}
"""
_DESCRIPTION = """\
KoPI-CC (Korpus Perayapan Indonesia)-CC is Indonesian Only Extract from Common Crawl snapshots ,each snapshots get extracted using ungoliant and get extra "filtering" using deduplication technique
"""
_HOMEPAGE = "https://huggingface.co/datasets/munggok/KoPI-CC"
_LICENSE = "CC0"
_URLS = {
"raw": "https://huggingface.co/datasets/munggok/KoPI-CC/resolve/main/{snapshot}/raw/id_meta_{index}.jsonl.zst",
"dedup": "https://huggingface.co/datasets/munggok/KoPI-CC/resolve/main/{snapshot}/dedup/oscar-{index:012d}.json.gz",
"neardup": "https://huggingface.co/datasets/munggok/KoPI-CC/resolve/main/{snapshot}/neardup/oscar-neardup-{index:012d}.json.gz",
"neardup_clean": "https://huggingface.co/datasets/munggok/KoPI-CC/resolve/main/{snapshot}/neardup_clean/cleaned_oscar-neardup-{index:012d}.json.gz",
}
_N_SHARDS_PER_SNAPSHOT = {
"2021_10": {"dedup": 132, "neardup": 120, "neardup_clean": 120},
"2021_17": {"raw": 31, "dedup": 47, "neardup": 41, "neardup_clean": 41},
"2021_21": {"raw": 63, "dedup": 37, "neardup": 33, "neardup_clean": 33},
"2021_25": {"raw": 31, "dedup": 32, "neardup": 28, "neardup_clean": 28},
"2021_31": {"raw": 35, "dedup": 47, "neardup": 42, "neardup_clean": 42},
"2021_39": {"raw": 35, "dedup": 44, "neardup": 38, "neardup_clean": 38},
"2021_43": {"raw": 35, "dedup": 44, "neardup": 39, "neardup_clean": 39},
"2021_49": {"dedup": 31, "neardup": 28, "neardup_clean": 28},
"2022_05": {"raw": 40, "dedup": 18, "neardup": 18, "neardup_clean": 35},
"2022_21": {"raw": 40, "dedup": 42, "neardup": 37, "neardup_clean": 37},
"2022_27": {"raw": 79, "dedup": 38, "neardup": 33, "neardup_clean": 33},
}
_SNAP_CONFIG = []
for m in list(_N_SHARDS_PER_SNAPSHOT.keys()):
ka = list(_N_SHARDS_PER_SNAPSHOT[m].keys())
conf = [m + "-" + a for a in ka]
_SNAP_CONFIG.extend(conf)
_SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING]
_ALL_CONFIG = ["all-raw", "all-dedup", "all-neardup", "all-neardup_clean"] + _SNAP_CONFIG
_SOURCE_VERSION = "2018.12.01"
_SEACROWD_VERSION = "2024.06.20"
def seacrowd_config_constructor(snapshot, schema, version):
"""Construct SEACrowdConfig"""
if schema != "source" and schema != "seacrowd_ssp":
raise ValueError(f"Invalid schema: {schema}")
if snapshot == "":
raise ValueError(f"Snapshot is required. Choose one of these Snapshot: {_ALL_CONFIG}.")
elif snapshot in _SNAP_CONFIG + _ALL_CONFIG:
return SEACrowdConfig(
name=f"{_DATASETNAME}_{snapshot}_{schema}",
version=datasets.Version(version),
description=f"KoPI-CC with {schema} schema for {snapshot}",
schema=schema,
subset_id="kopi_cc",
)
else:
raise ValueError(f"Invalid language: {snapshot}. Choose one of these snapshots: {_ALL_CONFIG}.")
class KoPICC(datasets.GeneratorBasedBuilder):
DEFAULT_CONFIG_NAME = "2021_17_dedup"
BUILDER_CONFIGS = [seacrowd_config_constructor(sn, "source", _SOURCE_VERSION) for sn in _ALL_CONFIG] + [seacrowd_config_constructor(sn, "seacrowd_ssp", _SEACROWD_VERSION) for sn in _ALL_CONFIG]
def _info(self):
if self.config.schema == "source":
features = datasets.Features(
{
"text": datasets.Value("string"),
"timestamp": datasets.Value("string"),
"url": datasets.Value("string"),
"meta": datasets.Value("string"),
}
)
elif self.config.schema == "seacrowd_ssp":
features = schemas.self_supervised_pretraining.features
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
name = self.config.name.replace("_" + self.config.schema, "")
name = name.replace(_DATASETNAME + "_", "")
split_name = name.split("-")
if split_name[0] == "all":
urls = []
keys = list(_N_SHARDS_PER_SNAPSHOT.keys())
idx = 0
if split_name[1] == "raw":
idx = 1
keys = [ur for ur in list(_N_SHARDS_PER_SNAPSHOT.keys()) if _N_SHARDS_PER_SNAPSHOT[ur].get("raw") is not None]
for m in keys:
urls.extend([_URLS[split_name[1]].format(snapshot=m, index=k + idx) for k in range(_N_SHARDS_PER_SNAPSHOT[m].get(split_name[1]))])
else:
urls = [_URLS[split_name[1]].format(snapshot=split_name[0], index=k + 1) for k in range(_N_SHARDS_PER_SNAPSHOT[split_name[0]][split_name[1]])]
path = dl_manager.download(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepaths": path, "split": "train", "type": split_name[1]},
),
]
def _generate_examples(self, filepaths, split, type):
"""This function returns the examples in the raw (text) form by iterating on all the files."""
id_ = 0
for filepath in filepaths:
if type == "raw":
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"]
if self.config.schema == "seacrowd_ssp":
yield id_, {"id": str(id_), "text": example["content"]}
id_ += 1
else:
yield id_, {"text": example["content"], "url": example["warc_headers"]["warc-target-uri"], "timestamp": example["warc_headers"]["warc-date"], "meta": json.dumps(meta)}
id_ += 1
else:
with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
for line in f:
if line:
example = json.loads(line)
if self.config.schema == "seacrowd_ssp":
yield id_, {"id": str(id_), "text": example["text"]}
id_ += 1
else:
yield id_, {"text": example["text"], "url": example["url"], "timestamp": example["timestamp"], "meta": example["meta"]}
id_ += 1
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