Positional Encoding

Positional encoding is a technique used primarily in natural language processing to provide information about the position of tokens in a sequence. Unlike recurrent neural networks, which inherently process data in order, transformer models require a method to capture the sequential nature of data. Positional encoding involves adding a unique vector to each token's embedding based on its position, allowing the model to differentiate between tokens based on their order. This approach is essential in tasks such as machine translation and text summarization, where understanding the sequence of words is crucial for accurate interpretation and generation of language.