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.
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