Flexible tokenizer implementation

When implementing tokenizers for AI models, ensure flexibility and robust behavior across different contexts:

1. Initialize tokenizers with all relevant parameters to maintain consistent behavior:

   tokenizer = Tokenizer(BPE(
       unk_token=str(unk_token), 
       dropout=dropout, 
       end_of_word_suffix=suffix
   ))
   

2. Use flexible patterns for detecting special tokens (like unknown tokens) rather than hardcoded strings:

   # Instead of checking for exactly "[UNK]"
   unk_token_regex = re.compile('(.{1}\b)?unk(\b.{1})?', flags=re.IGNORECASE)
   

This approach ensures tokenizers work consistently across different implementations and models, which is critical for reliable AI text processing pipelines and model interoperability.