## 4 Experimental Setup and Results
### Experimental Data
#### 4.4.3 Comprehensive Performance Comparison of Tibetan SBD under Four Pre-Trained Language Models  
1. Comparison of average metrics  
Table 8 and Figure 4 compare the average metrics on the four pre-trained language models to compare the performance of Tibetan SBD.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**TiBERT**} & \multicolumn{3}{c}{**Window = 3**} & \multicolumn{3}{c}{**Window = 5**} & \multicolumn{3}{c}{**Window = 10**} & \multicolumn{3}{c}{**Window = 15**} \\ \cline{2-13}  & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline TiBERT & 89.6 & 89.58 & 89.59 & 91.45 & 91.47 & 91.46 & 90.85 & 90.88 & 90.86 & 90.31 & 90.36 & 90.32 \\ TiBERT-CNN & 90.02 & 90 & 90.01 & 90.27 & 90.25 & 90.26 & 90.38 & 90.39 & 90.39 & 90.43 & 90.47 & 90.44 \\ TiBERT-RNN & 90.05 & 90.05 & 90.05 & 90.38 & 90.4 & 90.39 & 90.38 & 90.41 & 90.39 & 91.34 & 91.32 & 91.33 \\ TiBERT-RCNN & 90.01 & 89.98 & 89.99 & 90.4 & 90.35 & 90.37 & 90.42 & 90.42 & 90.42 & 90.63 & 90.6 & 90.62 \\ TBBERT- & 90.06 & 90.04 & 90.05 & 90.28 & 90.23 & 90.25 & 90.84 & 90.86 & 90.85 & 90.34 & 90.35 & 90.34 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Sentence boundary disambiguation in Tibetan based on TiBERT.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Multi-BERT**} & \multicolumn{3}{c}{**Window = 3**} & \multicolumn{3}{c}{**Window = 5**} & \multicolumn{3}{c}{**Window = 10**} & \multicolumn{3}{c}{**Window = 15**} \\ \cline{2-13}  & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline BERT & 89.48 & 89.53 & 89.44 & 89.44 & 89.49 & 89.42 & 89.01 & 89.04 & 88.93 & 39.94 & 63.19 & 48.94 \\ BERT-CNN & 88.35 & 88.30 & 88.12 & 89.96 & 89.96 & 89.86 & 88.78 & 88.84 & 88.77 & 39.94 & 63.19 & 48.94 \\ BERT-RNN & 88.68 & 88.73 & 88.63 & 90.64 & 90.68 & 90.64 & 90.43 & 90.44 & 90.34 & 69.75 & 70.67 & 69.42 \\ BERT-RCNN & 89.26 & 89.31 & 89.27 & 89.59 & 89.63 & 89.54 & 91.05 & 91.07 & 91.00 & 73.34 & 37.32 & 20.95 \\ BERT-DPCNN & 88.66 & 88.72 & 88.66 & 89.45 & 89.45 & 89.45 & 90.32 & 90.29 & 90.17 & 13.55 & 36.81 & 19.8 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Sentence boundary disambiguation in Tibetan based on Multi-BERT.  
From Table 8 and Figure 4, it can be seen that there are specific differences in the average SBD indicators of the five models on the four pre-trained language models: BERT (BPE), BERT (SP), TiBERT, and Multi BERT. The performance of BERT (BPE) and TiBERT pre-trained language models is relatively stable, with the highest average performance on the BERT (BPE) model, followed by the TiBERT model, and the lowest average performance on Multi-BERT. BERT (BPE) outperforms other models by four percentage points. Table 7 shows that on the Multi-BERT model, the performance indicators become unstable with the increase in windows. We know that, during text segmentation and pre-training, other pre-trained language models include all Tibetan letters (30 consonants and four vowels). At the same time, the vocabulary in Multi-BERT only contains a portion of Tibetan letters. Therefore, during training, when segmenting the corpus, a lot of \"[UNK]\" will be generated, which significantly impacts understanding the text's meaning.  
From Table 9, it can be seen that the predicted number of TP samples of different models is consistent with Tables 4 and 6, primarily, under the same window, the BERT(BPE) syncopation performs better than the TiBERT model, which in line with the experimental data in the previous study, and at the same time, for the same pre-trained language model, the number of TP samples in the process of window change is also consistent with the results in the previous table, so that the BERT model trained by using the BPE subword method is better suited for the Tibetan SBD task, and this provides a preprocessing method for subsequent downstream tasks and pre-trained language model datasets to be constructed.