How can I resolve out-of-vocabulary token issues in Hugging Face tokenizers

Question

With the help of code, can you explain how I can resolve out-of-vocabulary token issues in Hugging Face tokenizers?

Answer 1

To resolve out-of-vocabulary (OOV) token issues in Hugging Face tokenizers, use a tokenizer that supports subword tokenization (e.g., Byte-Pair Encoding or WordPiece). Alternatively, you can add new tokens to the vocabulary.

Here is the code example you refer to:

In the above code, we are using the following approaches:

• Subword Tokenization: Handles OOV words by breaking them into smaller subwords or characters.
• Adding Tokens: Extends the vocabulary with specific new tokens to handle domain-specific or custom vocabulary.
• Resizing Embeddings: Ensures the model can use the extended vocabulary during training or inference.

Hence, by referring to the above, you can resolve out-of-vocabulary token issues in Hugging Face tokenizers