SentencePiece

Description

SentencePiece is an unsupervised text tokenization tool that treats the input text as a raw stream of characters without requiring pre-tokenization. Developed by Google, it is particularly well-suited for languages with no explicit word boundaries, such as Chinese, Japanese, and Thai. SentencePiece is commonly used in Transformer-based models like T5 and XLM-R.

• Does not assume spaces signify word boundaries, allowing it to work across multiple languages seamlessly.
• Can train tokenization models using character-based approaches like BPE or unigram language models.
• Encodes text as subword units, including subword prefixes and suffixes when needed.
• Language-agnostic approach works well for multilingual models.
• Fully unsupervised, it learns tokenization rules directly from data without relying on pre-tokenized input.
• Flexible integration with BPE or Unigram-based models.
• May result in slightly less human-readable tokens (e.g., ▁ or unknown special tokens).
• Training can be more complex compared to simpler algorithms.
• SentencePiece is widely adopted in research and production settings for its robustness, flexibility, and effectiveness in multilingual and zero-shot NLP tasks.

Workflow

1. Training:

   • Train a model on raw text input, either using BPE or unigram language models.
   • Identify subwords and optimization rules directly from data.

2. Corpus Preparation: Does not require language-specific preprocessing like punctuation removal or token splitting.

3. Tokenization:

   • Encode a sentence as subword sequences based on the trained model.
   • Generate special tokens (e.g., <unk>, <pad>, etc.) as needed.

4. Inference: Apply the trained tokenization model to new text.

Example

Input text: this is a test.

Tokenization with SentencePiece might produce: ▁this ▁is ▁a ▁test

Here, the ▁ denotes a space, allowing the model to encode spaces as part of the tokenization schema.