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"""Samanantar dataset.""" |
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import re |
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import pandas as pd |
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import datasets |
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_CITATION = """\ |
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@misc{ramesh2021samanantar, |
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title={Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages}, |
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author={Gowtham Ramesh and Sumanth Doddapaneni and Aravinth Bheemaraj and Mayank Jobanputra and Raghavan AK and Ajitesh Sharma and Sujit Sahoo and Harshita Diddee and Mahalakshmi J and Divyanshu Kakwani and Navneet Kumar and Aswin Pradeep and Srihari Nagaraj and Kumar Deepak and Vivek Raghavan and Anoop Kunchukuttan and Pratyush Kumar and Mitesh Shantadevi Khapra}, |
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year={2021}, |
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eprint={2104.05596}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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""" |
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_DESCRIPTION = """\ |
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Samanantar is the largest publicly available parallel corpora collection for Indic languages: Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil, Telugu. The corpus has 49.6M sentence pairs between English to Indian Languages. |
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""" |
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_HOMEPAGE = "https://indicnlp.ai4bharat.org/samanantar/" |
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_LICENSE = "Creative Commons Attribution-NonCommercial 4.0 International" |
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_URLS = { |
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"0.3.0": "https://storage.googleapis.com/samanantar-public/V0.3/source_wise_splits.zip", |
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} |
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_LANGUAGES = ["as", "bn", "gu", "hi", "kn", "ml", "mr", "or", "pa", "ta", "te"] |
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PATH_PATTERN = re.compile(r"/(?:existing|created)/(?P<data_source>[^/]+)/") |
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class SamanantarConfig(datasets.BuilderConfig): |
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VERSION = datasets.Version("0.3.0") |
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def __init__(self, language=None, version=VERSION, **kwargs): |
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super().__init__(name=language, version=version, **kwargs) |
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self.language = language |
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class Samanantar(datasets.GeneratorBasedBuilder): |
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"""Samanantar dataset.""" |
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BUILDER_CONFIG_CLASS = SamanantarConfig |
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BUILDER_CONFIGS = [SamanantarConfig(language=language) for language in _LANGUAGES] |
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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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"idx": datasets.Value("int64"), |
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"src": datasets.Value("string"), |
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"tgt": datasets.Value("string"), |
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"data_source": datasets.Value("string"), |
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} |
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), |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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urls = _URLS[str(self.config.version)] |
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archive = dl_manager.download_and_extract(urls) |
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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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"paths": dl_manager.iter_files([archive]), |
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}, |
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), |
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] |
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def _generate_examples(self, paths): |
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id_ = 0 |
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for path in paths: |
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if "/created/" in path and f"/en-{self.config.language}/{self.config.language}_sents.tsv" in path: |
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match = PATH_PATTERN.search(path) |
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df = pd.read_csv(path, sep="\t") |
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for row in df.to_dict(orient="records"): |
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row.update(match.groupdict()) |
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yield id_, row |
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id_ += 1 |
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