Transformers is a deep learning model for natural language processing (NLP) that uses a transformer-based attention architecture. It was introduced in 2017 by Google AI researchers Vaswani et al.
The Transformer architecture is based on an encoder-decoder neural network that is used for PLN tasks such as machine translation, text generation and speech recognition. Unlike other PLN models, such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs), Transformers do not require a fixed input sequence and can handle variable-length inputs.
Attention is a key component in the Transformer architecture and allows the model to focus on specific parts of the input during the encoding process. The model also uses layers of input and output normalisation, and uses the technique of language pre-training to improve its generalisability.
Transformers has been used in a variety of PLN applications, including natural language generation, entity recognition and text classification. Its architecture has proven to be highly effective in PLN tasks, and is one of the most popular and widely used models today.
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