Good tokenization is the biggest difference in successful generative AI projects. It's a determining factor in the model's performance and accuracy. Below is a step-by-step guide on how to manage tokenization with recommended libraries and tools.
Handling Tokenization: Best Practices and Recommendations
Understanding Tokenization
Tokens: Based on the model's architecture, break the text into tokens, which can be words, subwords, or characters.
Encoding and Decoding: Understand how to convert text to token IDs (encoding) and vice versa from token IDs back to text (decoding).
Tokenizer Selection
A certain model might even have its tokenizer built in. It is best to pick the right tokenizer according to the model you are using (e.g., GPT-2, BERT, etc.).
Use of Special Tokens
Note that padding, end-of-sequence, and unknown tokens are important because that is how the model would react to the input.
Processing of Long Sequences
Use a truncate mechanism for very long text.
Design a strategy for splitting extremely long pieces of text so they do not exceed this model's maximum limits.
Processing in Bulk
Tokenization is used in bulk mainly when a big dataset needs the function to be fully utilized. Recommended Packages
Hugging Face Transformers :
It is the most versatile tool, and it encompasses a wide range of transformer models available in this library. Besides transformer models, it also brings a broad range of existing tokenizers for many supported models.
sentencePiece:
- An unsupervised text tokenizer and detokenizer that is particularly effective for language models. It works well with subword units.
BPE: One of the techniques that these models, including GPT-2, use in encoding pairs is this one. It starts by merging the most frequent pairs of bytes or characters iteratively.
Libraries such as Hugging Face tokenizers are very efficient in implementing BPE.
NLTK and SpaCy:
The libraries both allow for basic tokenization. They also allow for text data preprocessing, which are useful tools in addition to natural language processing.
OpenAI API:
If you're using OpenAI's models, they tokenize internally. You just feed your text and manage according to their pricing model for tokens.
