## 5 Conclusions  
In this study, we presented a novel approach to enhance the efficiency of the ALBERT model for natural language processing tasks. Utilizing Knowledge Distillation, we developed a system that transformed the existing ALBERT model into F-ALBERT, a significantly faster variant. Our research contributions can be categorized into two primary areas. Firstly, we proposed a speed-optimization technique for models based on Knowledge Distillation. We introduced an innovative knowledge representation method specifically designed for distilling ALBERT. Additionally, we developed effective strategies for knowledge transfer through LR-Distillation and IP-Distillation. Secondly, we presented a new Iterative Weight Pruning technique, which systematically pruned computational parameters at each instance of parameter reuse. We proposed a Partitioned Transition Module to implement this technique, which replaced the existing Transition Module within a layer. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Schedule** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline
**M** & 4 & 4 & 4 & 4 & 4 & 4 & 4 & 4 & 4 & 2 & 1 \\
**N** & 4 & 4 & 3 & 3 & 3 & 3 & 3 & 3 & 2 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: LR-ALBERT (large) IP-Distillation Schedules. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Parameters** & **Speedup** & **CoLA** & **RTE** & **MNLI** & **MRPC** & **QNLI** \\ \hline
**F-ALBERT (base)** & 12 M & 1.0\(\times\) & 48.3 & 72.3 & 80.6 & 87.5 & 88.4 \\
**TinyBERT** & 66 M & 1.2\(\times\) & 50.1 & 70.0 & 81.2 & 87.3 & 90.4 \\
**DistillBERT** & 66 M & 1.2\(\times\) & 45.3 & 65.5 & 80.5 & 85 & 89 \\
**ALP-KD** & 66 M & 1.2\(\times\) & - & 68.59 & 81.86 & 85.05 & 89.67 \\
**LAD** & 66 M & 1.2\(\times\) & - & 68.59 & 83.78 & 84.56 & 90.74 \\
**F-ALBERT (large)** & 18 M & 2.5\(\times\) & 52.1 & 75.2 & 82.6 & 89.2 & 89.6 \\
**BERT-PKD (large)** & 180 M & 3.0\(\times\) & 56.4 & 76.5 & 84.1 & 90 & 91.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance analysis of the proposed F-ALBERT compared to existing methodologies. The speedup factor measures how much faster F-ALBERT (base) is relative to the corresponding method. Additionally, the experiment recorded inference scores for various NLP tasks. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Schedule** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline
**X** & 4 & 4 & 4 & 4 & 2 & 1 \\
**Y** & 4 & 4 & 3 & 3 & 2 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: LR-ALBERT (base) IP-Distillation Schedules. We demonstrated that our final distilled model, F-ALBERT, substantially reduced the computational load by approximately half compared to the original ALBERT. Remarkably, despite the reduction in computational load, F-ALBERT retained nearly 98% of the original model's performance. The results of our research underlined the potential of distillation techniques for model optimization, suggesting a promising avenue for achieving a balance between high performance and computational efficiency in natural language processing tasks. In our research, the F-ALBERT model has demonstrated strengths in high parameter efficiency and low memory utilization. Future research will focus on enhancing throughput in embedded environments through the quantization, optimization, and expansion of the IP-ALBERT structure, leveraging these advantages. Quantization will enable its efficient execution in small-scale embedded settings, while the IP-ALBERT structure is anticipated to improve performance by exploring optimized parameter connections for each layer. Moreover, we will concentrate on the development and optimization of models specialized for specific industries and application areas through the extension of these technologies. In this way, we plan to explore performance enhancements and new possibilities in various environments and application fields, maximizing the efficiency and versatility of F-ALBERT.