What are the advantages of having a custom trained tokeniser

You are indeed training a Byte-Pair Encoding (BPE) tokenizer using the tokenizers library. Training your own tokenizer on a specific corpus (like the WikiText-103 dataset in your case) offers several significant practical advantages over using a pre-trained tokenizer:

1. Improved Performance on Domain-Specific Text:

Vocabulary Tailoring: Pre-trained tokenizers are trained on large, general-purpose datasets (like Common Crawl, Wikipedia). If your application deals with a highly specialized domain (e.g., medical texts, legal documents, code, specific scientific fields), the vocabulary of a general-purpose tokenizer might not be optimal. It might break down domain-specific terms into smaller, less meaningful subwords.

Reduced Out-of-Vocabulary (OOV) Tokens: By training on your specific data, the tokenizer learns to recognize and represent domain-specific vocabulary directly, significantly reducing the number of "[UNK]" (unknown) tokens. Fewer "[UNK]" tokens mean the model has more complete information to work with, leading to better understanding and performance in downstream tasks like text classification, generation, or question answering within that domain.

2. Efficiency and Model Size:

Smaller Vocabulary Size: You can tailor the vocabulary size to the specific needs of your data. A general-purpose tokenizer often has a very large vocabulary to cover a wide range of language. If your domain has a more limited vocabulary, you can train a smaller tokenizer, which can lead to smaller model sizes and potentially faster inference times, especially for models that have an embedding layer tied to the vocabulary size.

Optimized Token Lengths: The BPE algorithm learns merges based on the frequency of byte pairs in your training data. This can result in token lengths that are more efficient for your specific text. For example, if certain multi-character sequences are very common in your corpus, they are more likely to be merged into single tokens, leading to shorter input sequences for your model.

3. Better Handling of Specific Characters and Symbols:

Unicode and Special Characters: If your data contains a significant amount of specific Unicode characters, symbols, or formatting that might not be well-represented in a general-purpose tokenizer's vocabulary, training your own ensures these are handled more effectively.

Code Tokenization: For tasks involving code, a tokenizer trained on code will be much better at segmenting code into meaningful units (keywords, variable names, operators) compared to a tokenizer trained primarily on natural language.

4. Data Privacy and Security:

Avoiding Reliance on External Resources: Training your own tokenizer keeps the entire data processing pipeline within your control. This can be important for applications with strict data privacy or security requirements where sending data to external services for tokenization might be a concern.

5. Custom Pre-processing:

Integration with Specific Pre-processing Steps: You can tailor the pre-tokenizer (like the Whitespace() you're using) and the training process to align perfectly with any specific pre-processing steps you are applying to your data.

In your specific case with WikiText-103:

Training a tokenizer on the WikiText-103 dataset would be beneficial if you were specifically working on tasks related to this dataset or similar general-purpose English text. It would ensure that the tokenizer is well-suited to the vocabulary and structure of Wikipedia articles.

In summary, training your own tokenizer provides a level of customization and optimization that can lead to improved performance, efficiency, and better handling of domain-specific characteristics in your natural language processing applications. While it requires an initial investment of time and data, the benefits can be significant, especially when dealing with specialized or privacy-sensitive text.