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"""Itihasa Corpus.""" |
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import collections |
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import datasets |
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_DESCRIPTION = """\ |
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A Sanskrit-English machine translation dataset. |
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""" |
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_CITATION = """\ |
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@inproceedings{aralikatte-etal-2021-itihasa, |
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title = "Itihasa: A large-scale corpus for {S}anskrit to {E}nglish translation", |
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author = "Aralikatte, Rahul and |
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de Lhoneux, Miryam and |
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Kunchukuttan, Anoop and |
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S{\o}gaard, Anders", |
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booktitle = "Proceedings of the 8th Workshop on Asian Translation (WAT2021)", |
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month = aug, |
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year = "2021", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2021.wat-1.22", |
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pages = "191--197", |
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abstract = "This work introduces Itihasa, a large-scale translation dataset containing 93,000 pairs of Sanskrit shlokas and their English translations. The shlokas are extracted from two Indian epics viz., The Ramayana and The Mahabharata. We first describe the motivation behind the curation of such a dataset and follow up with empirical analysis to bring out its nuances. We then benchmark the performance of standard translation models on this corpus and show that even state-of-the-art transformer architectures perform poorly, emphasizing the complexity of the dataset.", |
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} |
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""" |
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_DATA_URL = "https://github.com/rahular/itihasa/archive/refs/heads/main.zip" |
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TranslateData = collections.namedtuple("TranslateData", ["url", "language_to_file"]) |
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class ItihasaConfig(datasets.BuilderConfig): |
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"""BuilderConfig for Itihasa.""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for Itihasa.""" |
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super(ItihasaConfig, self).__init__( |
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name="Itihasa", |
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description=_DESCRIPTION, |
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version=datasets.Version("1.0.0", ""), |
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**kwargs, |
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) |
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class Itihasa(datasets.GeneratorBasedBuilder): |
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"""Itihasa machine translation dataset.""" |
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BUILDER_CONFIGS = [ |
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ItihasaConfig() |
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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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{"translation": datasets.features.Translation(languages=("sn", "en"))} |
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), |
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supervised_keys=("sn", "en"), |
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homepage="http://www.rahular.com/itihasa/", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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dl_dir = dl_manager.download_and_extract(_DATA_URL) |
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source, target = "sn", "en" |
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path_tmpl = "{dl_dir}/itihasa-main/data/{split}.{lang}" |
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files = {} |
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for split in ("train", "dev", "test"): |
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files[split] = { |
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"source_file": path_tmpl.format(dl_dir=dl_dir, split=split, lang=source), |
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"target_file": path_tmpl.format(dl_dir=dl_dir, split=split, lang=target), |
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} |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs=files["train"]), |
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs=files["dev"]), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=files["test"]), |
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] |
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def _generate_examples(self, source_file, target_file): |
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"""This function returns the examples in the raw (text) form.""" |
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with open(source_file, encoding="utf-8") as f: |
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source_sentences = f.read().split("\n") |
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with open(target_file, encoding="utf-8") as f: |
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target_sentences = f.read().split("\n") |
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assert len(target_sentences) == len(source_sentences), "Sizes do not match: %d vs %d for %s vs %s." % ( |
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len(source_sentences), |
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len(target_sentences), |
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source_file, |
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target_file, |
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) |
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source, target = "sn", "en" |
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for idx, (l1, l2) in enumerate(zip(source_sentences, target_sentences)): |
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result = {"translation": {source: l1, target: l2}} |
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if all(result.values()): |
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yield idx, result |
