## 6 Comparison of the AraFastQA Model Versions and the ARBERT Model
### AraFastQA-Mini and AraFastQA-Base
In Table 6, a comprehensive overview of the F1 and EM scores for both models across different shot settings is shown, ranging from 16 shots to a full shot. The term \"shot\" refers to the number of training examples the model has been exposed to, with \"full shot\" indicating that training was conducted with the entire dataset. As shown in Figures 12 and 13, AraFastQA-base consistently outperformed AraFastQA-mini across all shot settings for both F1 and EM metrics. This trend suggests that the base model demonstrated stronger Arabic language understanding and processing capabilities in the context of QA tasks, which can be attributed to the model's pretraining on a large Arabic corpus. The incremental improvements observed with increasing shot sizes for both models indicate that the additional training examples contributed to each model's ability to generalize and respond accurately to queries. This trend was particularly evident in the full-shot setting, as both models exhibited their highest performance. Notably, the base model consistently maintained a larger margin of improvement over the mini model, which was pretrained on only 10 GB of data. The impact of corpus size on model performance was most noticeable in the full-shot setting, with the AraFastQA-base model significantly outperforming AraFastQA-mini. AraFastQA-base achieved an F1 score of 81.46 and an EM score of 66.12; in contrast, AraFastQA-mini's F1 score was 61.68, and its EM score was 45.05. The substantial differences highlight the significant advantage of using a large Arabic corpus for comprehensive tasks such as QA. The AraFastQA-base model also demonstrated better learning and adaptation capabilities, as evidenced by its consistent performance across different shot sizes. In the 64-shot setting, for example, the F1 and EM scores of the base model surpassed those of the mini model by 11.22 points and 9.45 percentages, respectively. The base model not only began with a higher F1 score, but also showed a steeper improvement curve as more shots were added. The performance differences between the AraFastQA-mini and AraFastQA-base models can be attributed to the significant difference in the pretraining data volume. The base model's superior accuracy was likely a result of pretraining on a large corpus of 112 GB, as compared to the mini model's 10 GB size. In NLP, the breadth and diversity of the pretraining corpus are crucial for a model to capture a wide range of linguistic patterns and contexts. The base model's exposure to a large Arabic corpus reflected a rich linguistic experience that equipped the model with a broad knowledge base for extracting answers and understanding complex language structures. Overall, AraFastQA-base's superior performance indicates its potential for practical applications requiring a high accuracy in understanding Arabic. However, this high accuracy may come at the cost of increased computational resources and inference times, which are typical trade-offs when deploying large, capable models in real-world scenarios. Figure 13: EM scores of the models. The collected Arabic corpora are significant for NLP tasks, such as NER and machine translation. These corpora provide valuable training data for NER models, enabling the accurate extraction and classification of named entities in Arabic texts. The availability of a diverse Arabic corpus can also improve the accuracy and fluency of machine translation systems, contributing to cross-lingual communication. Furthermore, the collected Arabic corpora can support cross-linguistic studies and comparative linguistic analyses. By comparing the Arabic corpus with corpora from other languages, researchers can gain insights into language structure, evolution, and linguistic phenomena, thereby advancing linguistic research and understanding of the Arabic language.