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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.
from posixpath import split
from typing import Dict, List, Tuple
import datasets
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)
import glob
_DATASETNAME = "indo4b"
_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
_UNIFIED_VIEW_NAME = DEFAULT_SEACROWD_VIEW_NAME
_LOCAL = False
_LANGUAGES = ["ind"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
_CITATION = """\
@inproceedings{wilie-etal-2020-indonlu,
title = "{I}ndo{NLU}: Benchmark and Resources for Evaluating {I}ndonesian
Natural Language Understanding",
author = "Wilie, Bryan and
Vincentio, Karissa and
Winata, Genta Indra and
Cahyawijaya, Samuel and
Li, Xiaohong and
Lim, Zhi Yuan and
Soleman, Sidik and
Mahendra, Rahmad and
Fung, Pascale and
Bahar, Syafri and
Purwarianti, Ayu",
booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the
Association for Computational Linguistics and the 10th International Joint
Conference on Natural Language Processing",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.aacl-main.85",
pages = "843--857",
abstract = "Although Indonesian is known to be the fourth most frequently used language
over the internet, the research progress on this language in natural language processing (NLP)
is slow-moving due to a lack of available resources. In response, we introduce the first-ever vast
resource for training, evaluation, and benchmarking on Indonesian natural language understanding
(IndoNLU) tasks. IndoNLU includes twelve tasks, ranging from single sentence classification to
pair-sentences sequence labeling with different levels of complexity. The datasets for the tasks
lie in different domains and styles to ensure task diversity. We also provide a set of Indonesian
pre-trained models (IndoBERT) trained from a large and clean Indonesian dataset (Indo4B) collected
from publicly available sources such as social media texts, blogs, news, and websites.
We release baseline models for all twelve tasks, as well as the framework for benchmark evaluation,
thus enabling everyone to benchmark their system performances.",
}
"""
_DESCRIPTION = """\
Indo4B is a large-scale Indonesian self-supervised pre-training corpus
consists of around 3.6B words, with around 250M sentences. The corpus
covers both formal and colloquial Indonesian sentences compiled from
12 sources, of which two cover Indonesian colloquial language, eight
cover formal Indonesian language, and the rest have a mixed style of
both colloquial and formal.
"""
_HOMEPAGE = "https://github.com/IndoNLP/indonlu"
_LICENSE = "CC0"
_LANGUAGES_MAP = {
"ind": "id",
"jav": "jv",
"sun": "su",
}
_URLS = {
"indo4b": "https://storage.googleapis.com/babert-pretraining/IndoNLU_finals/dataset/preprocessed/dataset_wot_uncased_blanklines.tar.xz",
}
_SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class Indo4B(datasets.GeneratorBasedBuilder):
"""Indo4B is a large-scale Indonesian self-supervised pre-training corpus
consists of around 3.6B words, with around 250M sentences."""
DEFAULT_CONFIG_NAME = "indo4b_source"
BUILDER_CONFIGS = [
SEACrowdConfig(
name="indo4b_source",
version=_SOURCE_VERSION,
description="Indo4B source schema",
schema="source",
subset_id="indo4b",
),
SEACrowdConfig(
name="indo4b_seacrowd_ssp",
version=_SEACROWD_VERSION,
description="Indo4B Nusantara schema",
schema="seacrowd_ssp",
subset_id="indo4b",
),
]
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"id": datasets.Value("string"),
"text": 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."""
url = _URLS["indo4b"]
path = dl_manager.download_and_extract(url) + "/processed_uncased_blanklines"
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": path,
"split": "train",
},
),
]
def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
counter = 0
for txt_path in glob.glob(f'{filepath}/*.txt'):
with open(txt_path, encoding="utf-8") as f:
if self.config.schema == "source":
for row in f:
if row.strip() != "":
yield (
counter,
{
"id": str(counter),
"text": row.strip(),
},
)
counter += 1
elif self.config.schema == "seacrowd_ssp":
for row in f:
if row.strip() != "":
yield (
counter,
{
"id": str(counter),
"text": row.strip(),
},
)
counter += 1 |