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import csv |
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import datasets |
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from datasets.tasks import Summarization |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """\ |
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@inproceedings{hasan-etal-2021-xl, |
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title = "{XL}-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages", |
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author = "Hasan, Tahmid and |
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Bhattacharjee, Abhik and |
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Islam, Md. Saiful and |
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Mubasshir, Kazi and |
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Li, Yuan-Fang and |
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Kang, Yong-Bin and |
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Rahman, M. Sohel and |
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Shahriyar, Rifat", |
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booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021", |
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month = aug, |
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year = "2021", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2021.findings-acl.413", |
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pages = "4693--4703", |
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} |
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""" |
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_DESCRIPTION = """Persian portion of the XLSum Dataset""" |
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_DOWNLOAD_URLS = { |
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"train": "https://huggingface.co/datasets/hezarai/xlsum-fa/resolve/main/xlsum-fa_train.csv", |
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"test": "https://huggingface.co/datasets/hezarai/xlsum-fa/resolve/main/xlsum-fa_test.csv", |
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} |
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class XLSumFaConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(XLSumFaConfig, self).__init__(**kwargs) |
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class XLSumFa(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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XLSumFaConfig( |
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name="xlsum-fa", |
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version=datasets.Version("1.0.0"), |
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description=_DESCRIPTION, |
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), |
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] |
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def _info(self): |
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text_column = "text" |
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summary_column = "summary" |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{text_column: datasets.Value("string"), |
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summary_column: datasets.features.Value("string")} |
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), |
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homepage="https://huggingface.co/datasets/hezarai/xlsum-fa", |
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citation=_CITATION, |
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task_templates=[Summarization(text_column=text_column, summary_column=summary_column)], |
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) |
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def _split_generators(self, dl_manager): |
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""" |
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Returns SplitGenerators. |
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""" |
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train_path = dl_manager.download_and_extract(_DOWNLOAD_URLS["train"]) |
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test_path = dl_manager.download_and_extract(_DOWNLOAD_URLS["test"]) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, gen_kwargs={"filepath": test_path} |
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), |
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] |
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def _generate_examples(self, filepath): |
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""" |
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Per each file_path read the csv file and iterate it. |
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For each row yield a tuple of (id, {"text": ..., "summary": ..., ...}) |
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Each call to this method yields an output like below: |
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``` |
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(123, {"text": "...", "summary": "..."}) |
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``` |
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""" |
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logger.info("⏳ Generating examples from = %s", filepath) |
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with open(filepath, encoding="utf-8") as csv_file: |
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csv_reader = csv.reader( |
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csv_file, quotechar='"', skipinitialspace=True |
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) |
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next(csv_reader, None) |
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for id_, row in enumerate(csv_reader): |
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text, label = row |
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yield id_, {"text": text, "summary": label} |
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