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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.
"""
This dataset collects all 353 books from the Thai National Historical Corpus 2 (TNHC2) corpus. The dataset has been cleaned to use text for pretraining models and NLP tasks. The TNHC2 corpus is a Thai old books corpus and all books are copyright expired according to Thai law (50 years after the author's death). More information on this corpus can be found here: https://www.arts.chula.ac.th/chulaseal/tnhc2/.
"""
from pathlib import Path
from typing import Dict, List, Tuple
import datasets
import pandas as pd
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Tasks, Licenses
_CITATION = """\
@dataset{phatthiyaphaibun_2024_10783421,
author = {Phatthiyaphaibun, Wannaphong},
title = {Thai TNHC2 Books},
month = mar,
year = 2024,
publisher = {Zenodo},
doi = {10.5281/zenodo.10783421},
url = {https://doi.org/10.5281/zenodo.10783421}
}
"""
_DATASETNAME = "thai_tnhc2_books"
_DESCRIPTION = """\
This dataset collects all 353 books from the Thai National Historical Corpus 2 (TNHC2) corpus. The dataset has been cleaned to use text for pretraining models and NLP tasks. The TNHC2 corpus is a Thai old books corpus and all books are copyright expired according to Thai law (50 years after the author's death). More information on this corpus can be found here: https://www.arts.chula.ac.th/chulaseal/tnhc2/.
"""
_HOMEPAGE = "https://www.arts.chula.ac.th/chulaseal/tnhc2/"
_LANGUAGES = ["tha"]
_LICENSE = Licenses.CC0_1_0.value
_LOCAL = False
_URLS = "https://huggingface.co/datasets/pythainlp/thai-tnhc2-books/resolve/main/data/train-00000-of-00001.parquet?download=true"
_SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class ThaiTnhc2BooksDataset(datasets.GeneratorBasedBuilder):
"""This dataset collects all 353 books from the Thai National Historical Corpus 2 (TNHC2) corpus. The dataset has been cleaned to use text for pretraining models and NLP tasks. The TNHC2 corpus is a Thai old books corpus and all books are copyright expired according to Thai law (50 years after the author's death). More information on this corpus can be found here: https://www.arts.chula.ac.th/chulaseal/tnhc2/."""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
BUILDER_CONFIGS = [
SEACrowdConfig(
name=f"{_DATASETNAME}_source",
version=SOURCE_VERSION,
description=f"{_DATASETNAME} source schema",
schema="source",
subset_id=f"{_DATASETNAME}",
),
SEACrowdConfig(
name=f"{_DATASETNAME}_seacrowd_ssp",
version=SEACROWD_VERSION,
description=f"{_DATASETNAME} SEACrowd schema",
schema="seacrowd_ssp",
subset_id=f"{_DATASETNAME}",
),
]
DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features({
"id": datasets.Value("string"),
"book": datasets.Value("string"),
"author": datasets.Value("string"),
"text": datasets.Value("string"),
})
elif self.config.schema == "seacrowd_ssp":
features = schemas.ssp_features
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
data_dir = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_dir,
},
),
]
def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
df = pd.read_parquet(filepath)
# Handle multiple books with the same id
df["id"] = df["id"] + "_" + df.groupby("id").cumcount().astype(str)
if self.config.schema == "source":
for i, row in df.iterrows():
yield i, {
"id": row["id"],
"book": row["book"],
"author": row["author"],
"text": row["text"],
}
elif self.config.schema == "seacrowd_ssp":
for i, row in df.iterrows():
yield i, {
"id": row["id"],
"text": row["text"],
}
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