## 4 Results and Discussion  
All experiments were executed using a single server instance containing two 16-core CPUs, 384 GB RAM, and an NVIDIA A40 graphical processing unit and tokenization of all examples, depending on the tokenizer, lasted from 46 seconds up to 1 minute. The average number of tokens used for the tokenization of examples in relation to the number of words within examples for all analyzed tokenizers is presented in Figures 3 and 4. Figure 3 contains results for the whole dataset, while Figure 4 contains a zoomed preview of the results where the number of words was lower than 500 in order to more clearly present comparison results for shorter examples that are more numerous. From the presented results, it can be noticed that on average, the SrBERTa v2 tokenizer generated fewer tokens compared to all other tokenizers, both for shorter and longer examples, while BERTic and NER4 Legal_SRB tokenizers obtained very similar results. It was noticed that, on average, for all examples, the SrBERTa v2 tokenizer generated 7.75% tokens less compared to the SrBERTa v1 tokenizer, 4.87% compared to the Jerteh-81 tokenizer, and 4.5% compared to the XLM-RoBERTa tokenizer. A comparative analysis of the tokenizers SrBERTa v2 and BERTic revealed that SrBERTa v2 produced, on average, 54.62% fewer tokens. A comparison between SrBERTa v2 and NER4 Legal_SRB tokenizers demonstrated the same result, with SrBERTa v2 generating 54.62% fewer tokens on average. Further, a deeper analysis revealed that in 30.56% of examples, tokenizers SrBERTa v2 and SrBERTa v1 generated the same number of tokens, while for tokenizers SrBERTa v2 and Jerteh-81, this percentage was 29.41%. Moreover, in 6.49% of examples, tokenizers SrBERTa v2 and XLM-RoBERTa generated the same number of tokens. However, this percentagewas 0.46% for both comparisons of SrBERTa v2 versus BERTic, and SrBERTa v2 versus NER4 Legal_SRB tokenizers. In the second part of the analysis, we compared the fertility measures for each of the analyzed results. The overall results, containing average and median fertility values based on all test examples, are presented in Table 1. From the presented results, it can be concluded that the tokenizer SrBERTa v2 obtained the best fertility results by having the lowest average and median values, followed by Jerteh-81 and XLM-RoBERTa tokenizers. BERTic and NER4 Legal_SRB obtained the highest fertility results. More precisely, the SrBERTa v2 tokenizer, on average, outperformed the previous SrBERTa version and XLM-RoBERTa by 9.03%, Jerteh-81 by 6.39%, and BERTic and NER4 Legal_SRB by 56.83%. For deeper fertility analysis, we have plotted the results in Figures 5 and 6 to observe the fertility values for different numbers of words. Results presented in Figure 5 are overall results generated based on the whole dataset, while results presented in Figure 6 are a preview of all examples up to 100 words. From the presented results, it was noticed that the tokenizer XLM-RoBERTa has the lowest average fertility values for examples shorter than  
Figure 4: The average number of tokens used for tokenization of examples in relation to the number of words within examples up to 500 words. Figure 3: The average number of tokens used for tokenization of examples in relation to the number of words within examples. three words. However, for examples longer than three words, both tokenizers SrBERTa v2 and Jerteh-81 obtained better results than the tokenizer XLM-RoBERTa. Further, it was also noticed that although the SrBERTa v2 tokenizer had the best average results for most of the sequence length, there were situations where Jerteh-81 obtained slightly better results. Lastly, we analyzed the number of tokens in relation to the word frequency. All test examples were divided into frequency-based groups, with group 1 containing words with