Transformer Models

Transformer models are a type of neural network architecture that was introduced in 2017 by Vaswani et al. in the paper titled

Attention Is All You Need

. This architecture has since become very popular in natural language processing (NLP) and has been used in various applications such as language translation, text summarization, and question answering.

How do Transformer Models Work?

Transformer models are based on the concept of self-attention. Self-attention is a mechanism that allows the model to focus on different parts of the input sequence when processing each element. This is done by computing an attention score between each pair of elements in the input sequence and using these scores to compute a weighted sum of the input elements. The resulting weighted sum is then used as the representation of the input sequence at that position.

The transformer model consists of an encoder and a decoder. The encoder takes the input sequence and generates a sequence of hidden representations. The decoder takes the output of the encoder and generates the output sequence.

One of the key innovations of the transformer model is the use of multi-head attention. Multi-head attention allows the model to attend to different positions of the input sequence at different representation subspaces. This improves the ability of the model to capture complex patterns in the input sequence.

Pretrained Transformer Models

Pretrained transformer models are trained on large amounts of text data and can be used for a variety of downstream tasks with minimal fine-tuning. Some of the most popular pretrained transformer models include BERT, GPT-2, and T5. These models have achieved state-of-the-art performance on various NLP tasks and have been widely adopted in both industry and academia.

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