Tokenization in Transformers v5: Simpler, Clearer, and More Modular

Transformers v5 introduces a significant redesign of the tokenization process, making it simpler, clearer, and more modular by separating tokenizer architecture from trained vocabulary, much like PyTorch separates model architecture from learned weights. This update enhances transparency by making the architecture of tokenizers explicit in class definitions, allowing for easier inspection of components such as normalizers and pre-tokenizers. It also consolidates the previously parallel slow and fast implementations into a single, preferred Rust-backed tokenizer system, eliminating redundancy and simplifying the user experience. The new system allows users to train custom tokenizers from scratch using templates that match any model's design, thus providing a more intuitive way to develop and customize tokenization processes. Additionally, the AutoTokenizer feature ensures that users can effortlessly load the correct tokenizer class for any specific model, maintaining the essential wrapper layer that adds model awareness and special token handling, while making the entire process more accessible and adaptable for practitioners.