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Sparse few-shot language model for Bengali achieves 90% sparsity

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Researchers have developed BnBERT-iPET, a novel approach to sparse few-shot language modeling specifically for Bengali. This method utilizes lottery ticket pruning to achieve 90% sparsity, significantly reducing computational requirements and memory footprint. The pruned model demonstrates performance comparable to larger, state-of-the-art models like Bangla Electra and Indic-BERT on Bengali downstream tasks. AI

IMPACT Enables more efficient development and deployment of NLP models for resource-constrained languages.

RANK_REASON The cluster contains an academic paper detailing a new model and methodology for NLP. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Sparse few-shot language model for Bengali achieves 90% sparsity

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The cluster contains an academic paper detailing a new model and methodology for NLP. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Sajib Hossain, Md Kamrus Samad, Anan Ghosh, Labib Imam Chowdhury, Nabeel Mohammed ·

    BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning

    arXiv:2608.05104v1 Announce Type: new Abstract: Deep neural networks have shown impressive success in NLP tasks owing to their complex structure and huge number of edges. Achieving state-of-the-art performance in natural language processing with a large pre-trained model such as …

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