Transformer

 A concise overview of Transformer-based embedding models, highlighting 4 key aspects:

Maximum Token Capacity: The longest sequence the model can process.

Embedding Size: The dimensionality of the generated embeddings.

Vocabulary Size: The number of unique tokens the model recognizes.

Tokenization Technique: The tokenization technique used to create the vocabulary.

In general, more advanced models tend to support longer input sequences while maintaining efficient embedding sizes for optimal performance.