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import csv |
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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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The AI4Bharat-IndicNLP dataset is an ongoing effort to create a collection of large-scale, |
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general-domain corpora for Indian languages. Currently, it contains 2.7 billion words for 10 Indian languages from two language families. |
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We share pre-trained word embeddings trained on these corpora. |
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We create news article category classification datasets for 9 languages to evaluate the embeddings. |
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We evaluate the IndicNLP embeddings on multiple evaluation tasks. |
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""" |
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_CITATION = """\ |
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@article{kunchukuttan2020indicnlpcorpus, |
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title={AI4Bharat-IndicNLP Corpus: Monolingual Corpora and Word Embeddings for Indic Languages}, |
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author={Anoop Kunchukuttan and Divyanshu Kakwani and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar}, |
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year={2020}, |
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journal={arXiv preprint arXiv:2005.00085}, |
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} |
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""" |
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_URLs = { |
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"malayalam_news": "https://huggingface.co/datasets/rajeshradhakrishnan/malayalam_news/blob/main/indicnlp-news-articles.tgz" |
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} |
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class MalayalamNewsConfig(datasets.BuilderConfig): |
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"""BuilderConfig for MalayalamNews.""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for MalayalamNews. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(MalayalamNewsConfig, self).__init__(**kwargs) |
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class MalayalamNews(datasets.GeneratorBasedBuilder): |
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"""Malayalam News topic classification dataset.""" |
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VERSION = datasets.Version("1.0.0") |
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BUILDER_CONFIGS = [ |
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MalayalamNewsConfig( |
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name="malayalam_news", version=VERSION, description="Malayalam News topic classification dataset." |
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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(names=["business", "entertainment", "sports", "technology"]), |
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} |
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), |
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homepage="https://github.com/AI4Bharat/indicnlp_corpus#indicnlp-news-article-classification-dataset", |
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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 _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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download_url = _URLs[self.config.name] |
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data_dir = dl_manager.download_and_extract(download_url) |
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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, "indicnlp-news-articles", "ml", "ml-train.csv"), |
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"split": "train", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"filepath": os.path.join(data_dir, "indicnlp-news-articles", "ml", "ml-valid.csv"), |
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"split": "validation", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"filepath": os.path.join(data_dir, "indicnlp-news-articles", "ml", "ml-test.csv"), |
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"split": "test", |
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}, |
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) |
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] |
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def _generate_examples(self, filepath, split): |
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"""Generate Malayalam News examples.""" |
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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='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True |
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) |
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for id_, row in enumerate(csv_reader): |
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label, description = row |
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text = description |
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yield id_, {"text": text, "label": label} |
