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import csv |
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import os |
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import datasets |
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csv.field_size_limit(int(1e6)) |
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_CITATION = """\ |
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@mastersthesis{chumpolsathien_2020, |
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title={Using Knowledge Distillation from Keyword Extraction to Improve the Informativeness of Neural Cross-lingual Summarization}, |
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author={Chumpolsathien, Nakhun}, |
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year={2020}, |
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school={Beijing Institute of Technology} |
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""" |
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_DESCRIPTION = """\ |
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ThaiSum is a large-scale corpus for Thai text summarization obtained from several online news websites namely Thairath, |
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ThaiPBS, Prachathai, and The Standard. This dataset consists of over 350,000 article and summary pairs |
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written by journalists. |
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""" |
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class ThaiSumConfig(datasets.BuilderConfig): |
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"""BuilderConfig for ThaiSum.""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for ThaiSum. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(ThaiSumConfig, self).__init__(**kwargs) |
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class Thaisum(datasets.GeneratorBasedBuilder): |
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"""ThaiSum: The largest dataset for Thai text summarization""" |
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_DOWNLOAD_URL = "https://archive.org/download/thaisum_datasets/data.zip" |
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_TRAIN_FILE = "train.csv" |
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_VAL_FILE = "valid.csv" |
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_TEST_FILE = "test.csv" |
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BUILDER_CONFIGS = [ |
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ThaiSumConfig( |
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name="thaisum", |
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version=datasets.Version("1.0.0"), |
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description="ThaiSum: The largest dataset for Thai text summarization", |
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), |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"title": datasets.Value("string"), |
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"body": datasets.Value("string"), |
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"summary": datasets.Value("string"), |
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"type": datasets.Value("string"), |
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"tags": datasets.Value("string"), |
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"url": datasets.Value("string"), |
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} |
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), |
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supervised_keys=("body", "summary"), |
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homepage="https://github.com/nakhunchumpolsathien/ThaiSum", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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arch_path = dl_manager.download_and_extract(self._DOWNLOAD_URL) |
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data_dir = os.path.join(arch_path, "data") |
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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={"filepath": os.path.join(data_dir, self._TRAIN_FILE)}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={"filepath": os.path.join(data_dir, self._VAL_FILE)}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"filepath": os.path.join(data_dir, self._TEST_FILE)}, |
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), |
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] |
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def _generate_examples(self, filepath): |
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"""Generate examples.""" |
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with open(filepath, encoding="utf-8") as f: |
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csv_reader = csv.reader(f) |
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next(csv_reader) |
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for id_, row in enumerate(csv_reader): |
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yield id_, { |
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"title": row[0], |
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"body": row[1], |
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"summary": row[2], |
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"type": row[3], |
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"tags": row[4], |
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"url": row[5], |
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} |
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