Transformer-based Encoder
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A Transformer-based Encoder is a neural network encoder that utilizes the transformer architecture (to process sequential input data into a continuous representation without relying on recurrent mechanisms).
- Context:
- It can (typically) include multiple encoder layers, each consisting of self-attention mechanisms and feed-forward neural networks.
- It can (typically) process input data in parallel, which significantly improves efficiency over traditional recurrent neural network (RNN) or long short-term memory (LSTM) based encoders.
- It can (often) be used in a wide range of natural language processing (NLP) tasks, including machine translation, text summarization, and sentence embedding generation.
- It can utilize position encoding to maintain the order of the input sequence, addressing the limitation of not inherently capturing sequential information due to its parallel processing nature.
- It can be part of a larger Transformer-based model, where it works in conjunction with a Transformer-based Decoder to perform tasks that involve both input understanding and generating outputs.
- ...
- Example(s):
- A BERT Encoder, which is designed to generate a rich representation of the input text by considering both left and right context in all layers.
- A Transformer-based LLM Encoder.
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- Counter-Example(s):
- A Convolutional Neural Network (CNN) Encoder , which uses convolutional layers to process spatial data.
- A Recurrent Neural Network (RNN) Encoder, which processes sequence data one element at a time in a recurrent manner.
- See: Transformer-based Neural Network, Self-Attention Mechanism, Positional Encoding, BERT, GPT.