How would you use byte-pair encoding BPE to train a tokenizer for a new foundation model

Question

Can i know How would you use byte-pair encoding (BPE) to train a tokenizer for a new foundation model?

Answer 1

You can use Byte-Pair Encoding (BPE) to train a tokenizer for a new foundation model by tokenizing a corpus, applying subword merging, and saving the learned vocabulary.

Here is the code snippet you can refer to:

In the above code, we are using the following key points:

• BPE Model (models.BPE()): Uses subword merging for efficient tokenization.

• Whitespace Pre-tokenization (pre_tokenizers.Whitespace()): Splits words before BPE encoding.

• Trainer (trainers.BpeTrainer()): Learns token merges from the dataset.

• Serialization (tokenizer.save()): Saves the tokenizer for later use.

Hence, BPE-based tokenization improves language model efficiency by handling rare words through subword segmentation.