Pragmatics
Machine Learning
A technique used in transformer models to incorporate sequence order information into position-independent attention mechanisms. It adds position-dependent signals to input embeddings.
Detailed Explanation
Positional Encoding enables transformer models to understand the order of tokens in a sequence by adding unique position-based signals to input embeddings. Since attention mechanisms are inherently order-agnostic, these encodings provide crucial information about token positions, allowing the model to comprehend the sequence structure, which is essential for tasks like language understanding, translation, and text generation.
Use Cases
- •Enhances language models by helping them grasp word order, improving translation accuracy and contextual understanding in natural language processing tasks.
Related Terms
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