Transformer-based Language Model
A transformer-based language model is a type of artificial neural network architecture that is used for natural language processing tasks such as language translation, text summarization, and sentiment analysis. It is based on the transformer architecture, which was introduced in 2017 by Vaswani et al. in their paper 'Attention Is All You Need'. The transformer-based language model uses self-attention mechanisms to process input sequences and generate output sequences. It is trained on large amounts of text data using unsupervised learning techniques such as masked language modeling and next sentence prediction. The transformer-based language model has achieved state-of-the-art performance on a wide range of natural language processing tasks and has been used in many applications such as chatbots, language translation, and text generation.
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