What are the most efficient algorithms for tokenizing long text sequences for GPT models

Question

Can you name the five most efficient algorithms for tokenizing long text sequences for GPT models?

Answer 1

The five most efficient algorithms for tokenizing long text sequences for GPT models are:

• Byte-Pair Encoding (BPE): It merges frequent pairs of characters or character sequences iteratively, allowing tokenizers to capture common subwords or morphemes. GPT-2 and many other transformers use it.
• Unique Language Model: Probabilistic model that splits text into subwords based on likelihood, focusing on maximizing token frequency over sequence length.
• Workpiece: Similar to BPE, the workpiece iteratively merges tokens but optimizes based on likelihood, choosing pairs that increase the probability of the model's vocabulary.
• Sentence pair: A versatile tokenizer that can apply both BPE and Unigram language models. It operates directly on raw text.
• Fast Tokenizers(like Hugging Face's Fast tokenizer): These tokenizers are optimized for speed and memory efficiency, implementing Rust with bindings of Python programming language.

The algorithms mentioned above are the most efficient algorithms for tokenizing long text sequences for GPT models.