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"""Arabic Poetry Metric dataset.""" |
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import os |
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import datasets |
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from datasets.tasks import TextClassification |
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_DESCRIPTION = """\ |
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Arabic Poetry Metric Classification. |
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The dataset contains the verses and their corresponding meter classes.\ |
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Meter classes are represented as numbers from 0 to 13. \ |
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The dataset can be highly useful for further research in order to improve the field of Arabic poems’ meter classification.\ |
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The train dataset contains 47,124 records and the test dataset contains 8316 records. |
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""" |
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_CITATION = """\ |
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@article{metrec2020, |
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title={MetRec: A dataset for meter classification of arabic poetry}, |
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author={Al-shaibani, Maged S and Alyafeai, Zaid and Ahmad, Irfan}, |
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journal={Data in Brief}, |
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year={2020}, |
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publisher={Elsevier} |
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} |
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""" |
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_DOWNLOAD_URL = "https://raw.githubusercontent.com/zaidalyafeai/MetRec/master/baits.zip" |
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class MetRecConfig(datasets.BuilderConfig): |
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"""BuilderConfig for MetRec.""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for MetRec. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(MetRecConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs) |
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class Metrec(datasets.GeneratorBasedBuilder): |
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"""Metrec dataset.""" |
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BUILDER_CONFIGS = [ |
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MetRecConfig( |
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name="plain_text", |
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description="Plain text", |
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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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"text": datasets.Value("string"), |
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"label": datasets.features.ClassLabel( |
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names=[ |
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"saree", |
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"kamel", |
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"mutakareb", |
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"mutadarak", |
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"munsareh", |
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"madeed", |
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"mujtath", |
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"ramal", |
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"baseet", |
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"khafeef", |
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"taweel", |
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"wafer", |
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"hazaj", |
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"rajaz", |
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] |
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), |
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} |
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), |
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supervised_keys=None, |
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homepage="https://github.com/zaidalyafeai/MetRec", |
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citation=_CITATION, |
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task_templates=[TextClassification(text_column="text", label_column="label")], |
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) |
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def _vocab_text_gen(self, archive): |
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for _, ex in self._generate_examples(archive, os.path.join("final_baits", "train.txt")): |
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yield ex["text"] |
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def _split_generators(self, dl_manager): |
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arch_path = dl_manager.download_and_extract(_DOWNLOAD_URL) |
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data_dir = os.path.join(arch_path, "final_baits") |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, gen_kwargs={"directory": os.path.join(data_dir, "train.txt")} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, gen_kwargs={"directory": os.path.join(data_dir, "test.txt")} |
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), |
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] |
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def _generate_examples(self, directory, labeled=True): |
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"""Generate examples.""" |
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with open(directory, encoding="UTF-8") as f: |
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for id_, record in enumerate(f.read().splitlines()): |
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label, bait = record.split(" ", 1) |
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yield str(id_), {"text": bait, "label": int(label)} |
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