Encoding Input Sequences
The component of the transformer architecture responsible for encoding input sequences is the encoder. It plays a crucial role in extracting meaningful representations from the input and capturing its contextual information.
Here's how the
encoder works:
- Tokenization: The input sequence is first divided into individual tokens, which can be words, characters, or subwords.
- Embedding: Each token is then mapped to a high-dimensional vector representation using an embedding layer. This representation captures the basic meaning of the token.
- Encoder Stack: The embedded tokens are fed into the encoder stack, which consists of multiple encoder blocks. Each block performs two main operations:
- Self-attention: This mechanism allows each token to attend to all other tokens in the sequence, capturing the relationships and dependencies between them. This helps to understand the context of each token within the entire sequence.
- Feedforward network: This is a standard neural network that adds non-linearity and helps the model learn complex relationships between the tokens.
- Output: After passing through all the encoder blocks, the input sequence is transformed into a sequence of hidden representations that capture the contextual meaning of each token.
These encoded representations are then passed to the decoder in the transformer architecture, which uses them to generate the output sequence, depending on the specific task (e.g., translation, summarization, etc.).
Therefore, the encoder plays a vital role in the transformer architecture by transforming the input sequence into a meaningful representation that can be used for various NLP tasks.
