## 4 Experimental Setup and Results
### Experimental Data  
The research found that there are fewer publicly available standardized electronic data in Tibetan, and it is necessary to collect experimental data that can satisfy the SBD task before conducting the research. The Tibetan data in this study is mainly a corpus of 13 types of news data obtained from the crawler of the Yunzang Tibetan search engine ([[https://www.yongzin.com/](https://www.yongzin.com/)]([https://www.yongzin.com/](https://www.yongzin.com/))), and the data are organized by the State Key Laboratory of Tibetan Information Processing and Application of Qinghai Normal University, which constructs a Tibetan sentence database of 465,669 sentences [54]. In this study, rule-based methods generate Gold standard data definitions and then manually proofread them to ensure the accuracy and reliability of the data. \"Generated by rule-based methods\" means data are created using predefined rules or algorithms. \"Manual proofreading\" is usually performed by experts or teams with specialized knowledge and experience, who will scrutinize and compare the data to ensure accuracy, completeness, and reasonableness. Statistically, the length of the units in the experimental data cut by a shad (\"[\"]) is from 3 to 50 syllables. This study divides the training set, test set, and validation set according to the ratio of 8:1:1. To verify the generalizability of the pre-trained language models on the task of sentence boundary disambiguation, this study experiments with the English SBD based on the BERT and Multi-BERT with English data from the \"Europarl\" corpus [55].  
For the experiments data in this study, a complete Tibetan sentence is randomly selected and labeled according to the training requirements. For instance, we have the following: \"-\"  
Tables 4 and 5 show that the performance of Tibetan SBD based on BERT (BPE) is about 5% higher than that of Tibetan SBD based on BERT (Sentencepiece). In the experiments of Tibetan SBD based on BERT (BPE), the highest F1 value is 95.32%, and the lowest is 94.9%; for BERT (BPE), on the BERT-CNN model, the highest F1 score is found when the window is 10, followed by the performance of the BERT-RCNN model when the window is 3, with an F1 score of 95.03%, and the performance of the model based on BERT (BPE) + deep learning model is 95.03%. The metrics differ when the model and window are different. However, the overall is around 95%. Table 5 shows the Tibetan SBD based on the BERT (SP) model practiced in this study, and the overall F1 of the Tibetan SBD performance based on BERT (SP) is between 88.76% and 91.47%. All the performances tend to increase during the process of increasing the window. Still, the maximum value is 3-4 percentage points different from BERT (BPE). Tables 4 and 5 show that the window change has a more noticeable effect on BERT (SP). At the same time, the performance of BERT(BPE) is more stable with the increase in the window, which also verifies the scientific and referable nature of the related literature in determining whether the shad (\"[\") is an accurate end-of-sentence marker or not by analyzing the words on the left side of the shad (\"[\") and the linguistic properties of the words.