Transformers play a critical role in generating contextual embeddings by leveraging self-attention mechanisms to model relationships between all input tokens simultaneously. Unlike traditional models, transformers process text bidirectionally, allowing them to capture rich, context-aware representations of words or sentences.
Models like BERT and GPT use transformer architectures to create embeddings where the meaning of each token depends on its surrounding context. For instance, the embedding for "bank" in "river bank" and "financial bank" will differ based on context, thanks to the transformer’s attention mechanism.
Transformers have become the backbone of state-of-the-art embedding models in NLP and multimodal tasks, enabling advanced capabilities like semantic understanding, cross-modal retrieval, and text-to-image generation. Their scalability and flexibility make them essential for modern AI systems.