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A hybrid tokenizer for Malayalam that combines rule-based Finite State Transducers (FST) for morphological analysis with Bi-LSTM-CRF neural networks for segmentation, designed to improve LLM vocabulary efficiency for agglutinative languages.
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The project addresses a valid technical gap: the poor performance of standard BPE tokenizers on morphologically rich, agglutinative languages like Malayalam. However, with zero stars and forks, it currently represents a personal research project or early-stage prototype. While the hybrid approach is linguistically sound, it lacks the ecosystem or validated performance metrics to be considered defensible.
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