Transformers and BERT are examples of which type of model?

Transformers and BERT are classified as large language models due to their architecture and the types of tasks they are designed to perform. Both leverage transformer architecture, which allows them to handle varying lengths of input sequences effectively by utilizing mechanisms like self-attention. This enables them to capture complex relationships and dependencies in text data, making them particularly adept at understanding context and semantics in natural language processing (NLP) tasks.

Large language models like BERT are pre-trained on vast datasets and can be fine-tuned for various specific applications, such as sentiment analysis, question answering, and translation. Their training involves predicting masked words in given sentences, which helps the model learn rich representations of language. This contextual awareness is a hallmark of large language models, distinguishing them from other types of models like convolutional neural networks (which are primarily used for image data) or recurrent neural networks (which process sequences in a different manner and have largely been supplanted by transformer models for many NLP tasks).