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"""The Arabic United nation Corpus dataset.""" |
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from __future__ import absolute_import, division, print_function |
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import glob |
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import os |
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import re |
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import datasets |
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_DESCRIPTION = """\ |
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The corpus is a part of the MultiUN corpus.\ |
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It is a collection of translated documents from the United Nations.\ |
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The corpus is download from the following website : \ |
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[open parallel corpus](http://opus.datasetsl.eu/) \ |
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""" |
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_CITATION = """\ |
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@inproceedings{eisele2010multiun, |
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title={MultiUN: A Multilingual Corpus from United Nation Documents.}, |
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author={Eisele, Andreas and Chen, Yu}, |
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booktitle={LREC}, |
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year={2010} |
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} |
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""" |
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URL = "https://object.pouta.csc.fi/OPUS-MultiUN/v1/mono/ar.txt.gz" |
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class AracorpusConfig(datasets.BuilderConfig): |
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"""BuilderConfig for BookCorpus.""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for BookCorpus. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(AracorpusConfig, self).__init__( |
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version=datasets.Version("1.0.0", "New split API (https://tensorflow.org/datasets/splits)"), **kwargs |
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) |
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class Aracorpus(datasets.GeneratorBasedBuilder): |
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"""BookCorpus dataset.""" |
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BUILDER_CONFIGS = [AracorpusConfig(name="plain_text", description="Plain text",)] |
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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({"text": datasets.Value("string"),}), |
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supervised_keys=None, |
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homepage="http://opus.datasetsl.eu/", |
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citation=_CITATION, |
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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): |
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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(URL) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"directory": arch_path}), |
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] |
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def _generate_examples(self, directory): |
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index=directory.rfind("datasets") |
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index=index+8 |
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url=directory[:index] |
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direct_name=directory[index+1:] |
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directory=url |
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files = [ |
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os.path.join(directory, direct_name), |
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] |
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_id = 0 |
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for txt_file in files: |
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with open(txt_file, mode="r") as f: |
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for line in f: |
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yield _id, {"text": line.strip()} |
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_id += 1 |
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