## 6 Comparison of the AraFastQA Model Versions and the ARBERT Model
### Evaluating AraFastQA-Base against Other Models  
The proposed model AraFastQA-base was additionally compared with the ARBERT model [73]. AraFastQA-base is a large-scale PML model designed specifically for modern standard Arabic (MSA), and it was trained on 61 GB of SMA text with vocabulary comprising 100 K words. ARBERT was built using the same architecture as BERT-base. In Table 6, Figures 12 and 13, the F1 scores of the AraFastQA-mini, AraFastQA-base, and ARBERT models on the TyDi QA dataset across various shot counts are illustrated. AraFastQA-base consistently outperforms ARBERT, particularly as the shot size increases, indicating its superior ability to handle the TyDi QA dataset. This is because the AraFastQA-base is trained to predict spans of tokens rather than just single tokens. This span-based prediction approach allows AraFastQA to capture more context and generate more coherent and accurate answers. Additionally, AraFastQA employs a dynamic strategy in predicting span tokens, which further enhances its ability to adapt to various questions and contexts, improving its overall performance. In contrast, ARBERT's single-token prediction approach limits its contextual understanding and response accuracy.  
Moreover, in Table 6, Figures 12 and 13, the performance of ArabicTransformer [71] on the TyDi QA dataset is displayed, measured using F1 and EM score across various shot counts. The ArabicTransformer model, an Arabic language model employing the Funnel Transformer architecture and the ELECTRA training objective, was pre-trained on a 44 GB Arabic corpus with a vocabulary size of 50,000 tokens. This model is designed to operate with lower computational and resource demands compared to models such as AraBERT and AraELECTRA.  
AraFastQA-base consistently outperforms ArabicTransformer-base, demonstrating its superior ability to handle the dataset, especially as the shot count increases. AraFastQA-base emerged as the best-performing model, consistently achieving the highest scores across all shot counts, highlighting its superior performance with larger datasets. Following AraFastQA-base, the ArabicTransformer-base model shows better results, particularly as the shot count increases, though it still trails behind AraFastQA-base. ARBERT ranks third, displaying steady improvement with increased shot counts, but ultimately being outperformed by both the AraFastQA-base and ArabicTransformer-base. Lastly, AraFastQA-mini is particularly effective in few-shot scenarios, outperforming ARBERT and ArabicTransformer-base at lower shot counts (16 to 128) due to its tailored design for limited training data. However, it lags behind the other models as the shot count increases.  
One of the key reasons AraFastQA-base outperforms ARBERT and ArabicTransformer is that it is trained on a larger corpus, has a better understanding of the morphology of the Arabic language, and can generate more accurate responses. Therefore, while AraFastQA-base demonstrates significant improvements compared to ARBERT, AraFastQA-mini does not show the same level of performance, likely due to it being trained on a smaller corpus.