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HalleluBERT: Let every token that has meaning bear its weight

2025-10-24 11:52:29
Raphael Scheible-Schmitt
arXiv_CL
           
Abstract

Transformer-based models have advanced NLP, yet Hebrew still lacks a large-scale RoBERTa encoder which is extensively trained. Existing models such as HeBERT, AlephBERT, and HeRo are limited by corpus size, vocabulary, or training depth. We present HalleluBERT, a RoBERTa-based encoder family (base and large) trained from scratch on 49.1~GB of deduplicated Hebrew web text and Wikipedia with a Hebrew-specific byte-level BPE vocabulary. Evaluated on NER and sentiment classification benchmarks, HalleluBERT outperforms both monolingual and multilingual baselines. HalleluBERT sets a new state of the art for Hebrew and highlights the benefits of fully converged monolingual pretraining.

Abstract (translated)

URL

https://arxiv.org/abs/2510.21372

PDF

https://arxiv.org/pdf/2510.21372.pdf

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