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A transformer is a deep learning architecture developed by Google and based on the multi-head attention mechanism, proposed in a 2017 paper "Attention Is All You Need". Text is converted to numerical representations called tokens, and each token is converted into a vector via looking up from a word embedding table.
A transformer is a type of artificial intelligence model that learns to understand and generate human-like text by analyzing patterns in large amounts of text data. Transformers are a current state-of-the-art NLP model and are considered the evolution of the encoder-decoder architecture.
A Transformer is a deep learning model that adopts the self-attention mechanism. This model also analyzes the input data by weighting each component differently. It is used primarily in artificial intelligence (AI) and natural language processing (NLP) with computer vision (CV).
Jan 6, 2023 · The Transformer Architecture. The Transformer architecture follows an encoder-decoder structure but does not rely on recurrence and convolutions in order to generate an output.
Apr 30, 2020 · Transformers are the rage in deep learning nowadays, but how do they work? Why have they outperform the previous king of sequence problems, like recurrent neural networks, GRU’s, and LSTM’s? You’ve probably heard of different famous transformers models like BERT, GPT, and GPT2.
Transformers have dominated empirical machine learning models of natural language pro-cessing. In this paper, we introduce basic concepts of Transformers and present key tech-niques that form the recent advances of these models. This includes a description of the standard Transformer architecture, a series of model refinements, and common applica-
Apr 20, 2023 · The transformer is a neural network component that can be used to learn useful representations of sequences or sets of data-points. The transformer has driven recent advances in natural language processing, computer vision, and spatio-temporal modelling.
May 24, 2024 · The transformer neural network is a novel architecture that aims to solve sequence-to-sequence tasks while handling long-range dependencies with ease. It was first proposed in the paper “Attention Is All You Need.” and is now a state-of-the-art technique in the field of NLP.
A Transformer is a model architecture that eschews recurrence and instead relies entirely on an attention mechanism to draw global dependencies between input and output. Before Transformers, the dominant sequence transduction models were based on complex recurrent or convolutional neural networks that include an encoder and a decoder.
🤗 Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch and TensorFlow — with a seamless integration between them. It's straightforward to train your models with one before loading them for inference with the other. Online demos. You can test most of our models directly on their pages from the model hub.
