Transformers revolutionized LLMs with their encoder-decoder architecture and attention mechanism. However, they struggle with computational overhead, especially in processing long sequences efficiently.
BERT has evolved from the original transformer model, enhancing data handling through bidirectional processing, learning context from both left and right side of data, which improves embedding quality.
Sentence BERT (SBERT) represents the forefront in sentence embeddings, facilitating better understanding and application in tasks such as retrieval-augmented generation (RAG) pipelines and semantic search.
Despite their effectiveness, transformers face challenges in handling longer sequences due to their computational limits, necessitating improvements like SBERT to maximize their performance in embeddings.
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