Understanding Transformers Part 2: Positional Encoding with Sine and Cosine
This article explains how transformers add position information to word embeddings using a sequence of sine and cosine waves. Each embedding dimension gets its value from a different wave, creating a unique positional encoding for each word.
Why it matters
Positional encoding is a crucial component of transformer models, enabling them to understand the order and structure of input sequences, which is essential for tasks like language modeling, translation, and text generation.
Key Points
- 1Transformers use sine and cosine waves to encode the position of words in a sequence
- 2Each embedding dimension is assigned a different wave, providing a unique positional value for each word
- 3The final positional encoding for a word is a vector combining the values from all the waves
Details
Transformers need to understand the order of words in a sequence, not just the words themselves. To achieve this, they use a positional encoding technique that assigns a unique vector to each word based on its position in the sequence. This positional encoding is created by combining values from a set of sine and cosine waves, where each wave corresponds to a specific dimension of the word embedding. The waves provide a continuous, periodic signal that encodes the position information. By combining the values from all the waves, transformers generate a unique positional encoding vector for each word, which is then added to the word's embedding before being processed by the transformer layers.
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