## 4 Experimental Setup and Results
### Experimental Data
#### 4.4.2 Tibetan SBD Based on Publicly Available BERT Pre-Trained Language Models  
To compare the effectiveness of the pre-trained language models in this study, the Tibetan SBD based on the publicly available pre-trained language model TiBERT and the multilingual model Multi-BERT are produced, and the Tibetan SBD based on the BERT+deep learning (BERT-CNN, BERT-RNN, BERT-RCNN, BERT-DPCNN) model is also compared with the performance. During the study of SBD based on a pre-trained language model, it is found that there are also Tibetan elements present in Multi-BERT, namely, \"[\"], \"[\"], \"[\"], \"[\"], \"[\"], \"[\"], \"[\"], \"[\"], \"[\"], \"[\"], \"[\"], \"[\"], \"[\"], \"[\"], \"[\"], \"[\"], \"[\"], \"[\"], \"[\"], \"[\"], \"[\"], \"[\"], \"[\"], \"[\"], and \"[\"], which contain the commonly used Tibetan end-of-sentence punctuation mark \"[\"] and the syllable separator \"[\"]. Multi-BERT cuts words based on Wordpiece, while TiBERT cuts words based on Sentencepiece. The experimental results based on TiBERT and Multi-BERT are shown in Tables 6 and 7. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**BERT (BPE)**} & \multicolumn{2}{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 & 94.91 & 94.92 & 94.9 & 94.62 & 94.59 & 94.55 & 95.19 & 95.2 & 95.19 & 94.91 & 94.89 & 94.9 \\ BERT-CNN & 94.94 & 94.93 & 94.9 & 94.76 & 94.73 & 94.69 & 95.32 & 95.32 & 95.32 & 94.15 & 94.16 & 94.15 \\ BERT-RNN & 94.82 & 94.81 & 94.78 & 94.9 & 94.9 & 94.88 & 95.12 & 95.12 & 95.1 & 94.73 & 94.77 & 94.71 \\ BERT-RCNN & 95.04 & 95.05 & 95.03 & 94.81 & 94.79 & 94.76 & 95.24 & 95.24 & 95.23 & 94.74 & 94.75 & 94.74 \\ BERT-DPCNN & 94.3 & 94.3 & 94.27 & 94.94 & 94.95 & 94.94 & 95.26 & 95.26 & 95.24 & 94.49 & 94.5 & 94.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Tibetan SBD based on BERT (BPE) pre-trained language models. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**BERT (SP)**} & \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 & 88.55 & 88.58 & 88.56 & 90.21 & 90.26 & 90.20 & 90.60 & 90.63 & 90.61 & 91.47 & 91.49 & 91.47 \\ BERT-CNN & 88.57 & 88.55 & 88.56 & 90.51 & 90.51 & 90.81 & 90.88 & 90.88 & 90.61 & 90.64 & 90.62 \\ BERT-RNN & 88.74 & 88.75 & 90.34 & 90.36 & 90.35 & 94.47 & 90.51 & 90.47 & 45.23 & 38.54 & 37.33 \\ BERT-RCNN & 88.36 & 88.38 & 88.37 & 90.49 & 90.53 & 90.50 & 90.88 & 90.91 & 90.88 & 90.57 & 90.59 & 90.51 \\ BERT-DPCNN & 88.75 & 88.77 & 88.76 & 90.35 & 90.36 & 90.35 & 91.34 & 91.37 & 91.34 & 91.15 & 91.16 & 91.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Tibetan SBD based on BERT(SP) pre-trained language models. As can be seen from Table 6, the performance of the TiBERT-based Tibetan SBD method is close to that of BERT (SP), with an overall F1 of 89.99-91.33%. The performance of the TiBERT-based pre-trained language model is more stable when the window is changed. The TiBERT model has the most considerable F1 value at a window of 5, 91.46%, followed by the TiBERT-RNN at an F1 value when the window is 15, which is 91.33%, and under the same window setting, the change of the model has a more subtle effect on the accuracy. This also proves that the Tibetan-based BERT pre-training model will have room for improvement after changing the subword method and training corpus. Expanding the size of the Tibetan pre-trained language model corpus is necessary to train an efficient Tibetan pre-trained language model.