🤯 Did You Know (click to read)
Alternative methods, like learned positional embeddings, have also been used in Transformers to encode sequence information dynamically.
Transformers add positional encoding vectors to input embeddings, encoding position via sine and cosine functions at different frequencies. This allows the model to distinguish between the first and last tokens and to reason about relative positions, essential for language understanding and generation tasks.
💥 Impact (click to read)
Positional encoding ensures the Transformer model can capture word order dependencies, critical for grammar, semantics, and translation.
Developers benefit from understanding positional encoding to adapt Transformers for tasks like time series prediction or sequence labeling, extending its applications beyond NLP.
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