The cheat sheet also introduces many concepts, including: The multi-head attention (MHA) mechanism performs parallel attention calculations to generate outputs representing diverse text relationships. Transformers consist of stacks of encoders and/or decoders, which use position embeddings to understand word order.

Sep 8, 2024 · Cross-attention allows one sequence (e.g., encoder output) to attend to another (e.g., decoder input), widely used in models like T5 and BART.

The seq2seq (sequence to sequence) model is a type of encoder-decoder deep learning model commonly employed in natural language processing that uses recurrent neural networks like LSTM to generate output. seq2seq can generate output token by token or character by character.

Auto-regressive (or decoder-only) models like GPT that are great at text generation; Sequence-to-sequence (or encoder-decoder) models like BART and T5 that are suitable for summarization and translation.

Oct 20, 2024 · In this article, we’ll explore the core components of the decoder: input embeddings, positional encoding, masked self-attention, encoder-decoder attention, layer normalization, residual...

- Encoder: Composed of multiple layers of self-attention and feed-forward neural networks. - Decoder: Similar to the encoder but also includes an additional attention mechanism over the encoder's output.

Jun 18, 2023 · Encoding and Decoding: The encoder takes individual tokens from the input sequence and produces a state that represents each token and the previously processed tokens.

Apr 29, 2024 · CTranslate2 is a powerful tool for efficient inference with Transformer models, offering fast execution, reduced memory usage, and support for various model types and frameworks. It supports several model types, such as encoder-decoder, decoder-only, and encoder-only models, including popular ones like Transformer, GPT-2, and BERT.

Autoencoders are a type of neural network that are commonly used in unsupervised machine learning tasks. They are designed to reconstruct input data by learning an efficient encoding and decoding mechanism. Autoencoders have various applications, such as dimensionality reduction, anomaly detection, and image denoising. 1. What is an Autoencoder?

Use our free JWT Decoder to instantly decode and verify JSON Web Tokens (JWT). Analyze payload, headers, and claims for secure API authentication and debugging.