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"""Arabic Billion Words Corpus""" |
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import os |
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import re |
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import datasets |
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_CITATION = """\ |
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@article{el20161, |
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title={1.5 billion words arabic corpus}, |
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author={El-Khair, Ibrahim Abu}, |
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journal={arXiv preprint arXiv:1611.04033}, |
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year={2016} |
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} |
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""" |
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_DESCRIPTION = """\ |
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THIS IS A FORK FOR LOCAL USAGE. |
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Abu El-Khair Corpus is an Arabic text corpus, that includes more than five million newspaper articles. |
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It contains over a billion and a half words in total, out of which, there are about three million unique words. |
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The corpus is encoded with two types of encoding, namely: UTF-8, and Windows CP-1256. |
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Also it was marked with two mark-up languages, namely: SGML, and XML. |
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""" |
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_HOMEPAGE = "https://huggingface.co/datasets/arabic_billion_words" |
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_URL = "https://huggingface.co/datasets/oserikov/arabic_billion_words/resolve/main/dataset/new_data_" |
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_URLs = { |
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"Alittihad": _URL + "Alittihad_XML_utf_8.rar", |
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"Almasryalyoum": _URL + "Almasryalyoum_XML_utf_8.rar", |
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"Almustaqbal": _URL + "Almustaqbal_XML_utf_8.rar", |
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"Alqabas": _URL + "Alqabas_XML_utf_8.rar", |
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"Echoroukonline": _URL + "Echoroukonline_XML_utf_8.rar", |
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"Ryiadh": _URL + "Ryiadh_XML_utf_8.rar", |
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"Sabanews": _URL + "Sabanews_XML_utf_8.rar", |
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"SaudiYoum": _URL + "SaudiYoum_XML_utf_8.rar", |
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"Techreen": _URL + "Techreen_XML_utf_8.rar", |
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"Youm7": _URL + "Youm7_XML_utf_8.rar", |
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} |
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MISSPELLED_TAGS = { |
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"Dateline": ["Dateline", "dateline"], |
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"Headline": ["Headline", "Healine"], |
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"Text": ["Text"], |
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"URL": ["URL"], |
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} |
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TAG_PATTERNS = { |
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tag: [re.compile(rf".*?<{label}>(.*?)</{label}>.*?", re.MULTILINE | re.DOTALL) for label in labels] |
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for tag, labels in MISSPELLED_TAGS.items() |
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} |
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class ArabicBillionWords(datasets.GeneratorBasedBuilder): |
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"""Arabic Billion Words Corpus""" |
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VERSION = datasets.Version("1.1.0") |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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name="Alittihad", version=VERSION, description="This part of dataset covers Alittihad news paper" |
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), |
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datasets.BuilderConfig( |
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name="Almasryalyoum", version=VERSION, description="This part of dataset covers Almasryalyoum news paper" |
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), |
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datasets.BuilderConfig( |
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name="Almustaqbal", version=VERSION, description="This part of dataset covers Almustaqbal news paper" |
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), |
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datasets.BuilderConfig( |
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name="Alqabas", version=VERSION, description="This part of dataset covers Alqabas news paper" |
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), |
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datasets.BuilderConfig( |
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name="Echoroukonline", version=VERSION, description="This part of dataset covers Echoroukonline news paper" |
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), |
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datasets.BuilderConfig( |
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name="Ryiadh", version=VERSION, description="This part of dataset covers Ryiadh news paper" |
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), |
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datasets.BuilderConfig( |
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name="Sabanews", version=VERSION, description="This part of dataset covers Sabanews news paper" |
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), |
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datasets.BuilderConfig( |
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name="SaudiYoum", version=VERSION, description="This part of dataset covers SaudiYoum news paper" |
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), |
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datasets.BuilderConfig( |
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name="Techreen", version=VERSION, description="This part of dataset covers Techreen news paper" |
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), |
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datasets.BuilderConfig( |
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name="Youm7", version=VERSION, description="This part of dataset covers Youm7 news paper" |
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), |
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] |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"url": datasets.Value("string"), |
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"head_line": datasets.Value("string"), |
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"date": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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} |
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) |
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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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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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my_urls = _URLs[self.config.name] |
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data_dir = dl_manager.download_and_extract(my_urls) |
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my_file_name = f"{self.config.name}_utf_8.xml" |
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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": os.path.join(data_dir, my_file_name), |
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}, |
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), |
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] |
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def _generate_examples(self, filepath): |
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"""Yields examples.""" |
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data_tag = self.config.name |
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pattern = re.compile(rf".*?<{data_tag}(.*?)</{data_tag}.*?", re.MULTILINE | re.DOTALL) |
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key = 0 |
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lines = "" |
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with open(filepath, mode="r", encoding="utf-8") as f: |
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for i, line in enumerate(f): |
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lines += line |
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if f"</{data_tag}" in line: |
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match = pattern.match(lines) |
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lines = "" |
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if match: |
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record = match.group(1) |
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text = self._clean_text(self._extract_tag("Text", record)) |
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url = self._extract_tag("URL", record) |
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head_line = self._clean_text(self._extract_tag("Headline", record)) |
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date = self._extract_tag("Dateline", record) |
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yield key, {"url": url, "head_line": head_line, "date": date, "text": text} |
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key += 1 |
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@staticmethod |
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def _extract_tag(tag, text): |
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for pattern in TAG_PATTERNS[tag]: |
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match = pattern.match(text) |
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if match: |
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return match.group(1) |
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return "" |
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@staticmethod |
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def _clean_text(text): |
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return text.replace("?", "") |