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""" |
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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/. |
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""" |
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from pathlib import Path |
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from typing import Dict, List, Tuple |
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import datasets |
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import pandas as pd |
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from seacrowd.utils import schemas |
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from seacrowd.utils.configs import SEACrowdConfig |
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from seacrowd.utils.constants import Tasks, Licenses |
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_CITATION = """\ |
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@dataset{phatthiyaphaibun_2024_10783421, |
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author = {Phatthiyaphaibun, Wannaphong}, |
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title = {Thai TNHC2 Books}, |
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month = mar, |
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year = 2024, |
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publisher = {Zenodo}, |
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doi = {10.5281/zenodo.10783421}, |
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url = {https://doi.org/10.5281/zenodo.10783421} |
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} |
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""" |
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_DATASETNAME = "thai_tnhc2_books" |
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_DESCRIPTION = """\ |
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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/. |
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""" |
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_HOMEPAGE = "https://www.arts.chula.ac.th/chulaseal/tnhc2/" |
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_LANGUAGES = ["tha"] |
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_LICENSE = Licenses.CC0_1_0.value |
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_LOCAL = False |
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_URLS = "https://huggingface.co/datasets/pythainlp/thai-tnhc2-books/resolve/main/data/train-00000-of-00001.parquet?download=true" |
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_SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING] |
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_SOURCE_VERSION = "1.0.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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class ThaiTnhc2BooksDataset(datasets.GeneratorBasedBuilder): |
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"""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/.""" |
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
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SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) |
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BUILDER_CONFIGS = [ |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_source", |
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version=SOURCE_VERSION, |
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description=f"{_DATASETNAME} source schema", |
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schema="source", |
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subset_id=f"{_DATASETNAME}", |
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), |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_seacrowd_ssp", |
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version=SEACROWD_VERSION, |
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description=f"{_DATASETNAME} SEACrowd schema", |
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schema="seacrowd_ssp", |
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subset_id=f"{_DATASETNAME}", |
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), |
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] |
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DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source" |
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def _info(self) -> datasets.DatasetInfo: |
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if self.config.schema == "source": |
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features = datasets.Features({ |
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"id": datasets.Value("string"), |
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"book": datasets.Value("string"), |
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"author": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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}) |
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elif self.config.schema == "seacrowd_ssp": |
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features = schemas.ssp_features |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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"""Returns SplitGenerators.""" |
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data_dir = dl_manager.download_and_extract(_URLS) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"filepath": data_dir, |
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}, |
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), |
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] |
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def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]: |
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"""Yields examples as (key, example) tuples.""" |
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df = pd.read_parquet(filepath) |
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df["id"] = df["id"] + "_" + df.groupby("id").cumcount().astype(str) |
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if self.config.schema == "source": |
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for i, row in df.iterrows(): |
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yield i, { |
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"id": row["id"], |
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"book": row["book"], |
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"author": row["author"], |
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"text": row["text"], |
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} |
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elif self.config.schema == "seacrowd_ssp": |
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for i, row in df.iterrows(): |
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yield i, { |
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"id": row["id"], |
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"text": row["text"], |
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} |
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