# A Comparative Evaluation of Transformers and Deep Learning Models for Arabic Meter Classification
## 2 Literature Review  
Deep learning methods have been extensively utilized in numerous studies to address Arabic meter classification. Notably, the work by [PERSON] et al. [26] demonstrated significant progress by modeling the sequential nature of poetic lines using bidirectional recurrent neural networks (RNNs). Their approach effectively captured rhythmic patterns in Arabic poetry, providing a strong foundation for further exploration using advanced models such as Transformers. Several efforts have aimed to develop algorithms for identifying meters in contemporary Arabic poetry [4]. Some approaches focus on detecting features of classical poetry, including rhythm, punctuation, and alignment, although however, they merely differentiate poetic from non-poetic texts [27]. Other techniques attempt to identify specific meters by converting verses into 'Arud' script using Mel Frequency and Linear Predictive Cepstral Coefficients characteristics [28]. Achieving performance levels of up to 75% accuracy, [PERSON] et al. [29] emphasized that automated meter recognition requires intricate data processing. Their method involves syllables segmentation, phoneme conversion, and the alignment of resulting patternswith predefined meter templates. Building on [PERSON]'s approach, [PERSON] et al. [30] were noteworthy in automating Arabic meter classification. Their method supports educators and students in understanding prosody and accurately identifying poetic meters, even in complex scenarios. [PERSON] et al. [31] achieved high accuracy in meter detection by employing a model architecture comprising an embedding layer, five Bi-LSTM layers, a softmax activation function, and an output layer. Similarly, [PERSON] et al. [32] developed a neural network focused on enhancing diacritical understanding, which is crucial for improving both understanding and meter recognition accuracy. [PERSON] et al. [33] introduced a robust method based on leveraging contextual semantic embeddings and transformer models for Arabic biomedical search application. Their methodology involved preprocessing biomedical text, generating vector representations, and fine-tuning several transformer variants--including BERT, AraBERT, Biomedical BERT, RoBERTa, and Distilled--using a specialized Arabic biomedical dataset. Their model achieved an F1-score of 93.35% and an accuracy of 93.31%. Despite this strong performance, the authors identified key limitations, including Arabic's complex morphology and the scarcity of domain-specific datasets. They emphasized the necessity of enriched contextual embeddings and expanded datasets to further improve transformer model performance in Arabic biomedical NLP applications. Combining AraBERT's contextual comprehension with Long Short-Term Memory (LSTM) for sequence modeling, [PERSON] et al. [34] proposed a sentiment analysis model. Its performance was benchmarked against traditional ML and DL models using various vectorization strategies across four Arabic benchmark datasets. [PERSON] et al. [13] applied LSTM and convolutional neural network models, optimized using the Hawks optimization method, on the MetRec dataset for meter classification, achieving an accuracy of 98% with precision and recall scores of 86.5%. In sentiment analysis, ensemble learning--where multiple models are combined to enhance classification accuracy--has proven highly effective. Studies show that integrating multiple deep learning models can outperform individual architectures in Arabic sentiment analysis tasks [35; 36]. For instance, [PERSON] et al. [37] employed a hybrid model combining convolutional and LSTM networks, where the convolutional layers captured local features and LSTM layers captured global context. AraGPT-2, the Arabic adaptation of Generative Pre-trained Transformer-2, aims to generate synthetic text to improve Arabic classification tasks. [PERSON][PERSON] et al.