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Results for "pretrained language model"

AraPoemBERT: A Pretrained Language Model for Arabic Poetry Analysis

arXiv · Mar 19

The paper introduces AraPoemBERT, an Arabic language model pretrained exclusively on 2.09 million verses of Arabic poetry. AraPoemBERT was evaluated against five other Arabic language models on tasks including poet's gender classification (99.34% accuracy) and poetry sub-meter classification (97.79% accuracy). The model achieved state-of-the-art results in these and other downstream tasks, and is publicly available on Hugging Face. Why it matters: This specialized model advances Arabic NLP by providing a new state-of-the-art tool tailored for the nuances of classical Arabic poetry.

On the importance of Data Scale in Pretraining Arabic Language Models

arXiv · Jan 15

This paper studies the impact of data scale on Arabic Pretrained Language Models (PLMs). Researchers retrained BERT-base and T5-base models on large Arabic corpora, achieving state-of-the-art results on the ALUE and ORCA benchmarks. The analysis indicates that pretraining data volume is the most important factor for performance. Why it matters: This work provides valuable insights into building effective Arabic language models, emphasizing the importance of large, high-quality datasets for advancing Arabic NLP.

Supporting Undotted Arabic with Pre-trained Language Models

arXiv · Nov 18

The paper examines the performance of pre-trained Arabic language models on Arabic text intentionally stripped of diacritical dots to evade content classification. It proposes methods to support these "undotted" texts without retraining the models. The proposed methods achieve nearly perfect performance on one downstream task. Why it matters: The research highlights a vulnerability in Arabic NLP and offers solutions to maintain performance in the face of adversarial text manipulation.