Transformers have really changed the NLP world, in part due to their self-attention component. But this component is problematic in the sense that it has quadratic computational and memory growth with sequence length, due to the QK^T diagonals (Questions, Keys diagonals) in the self-attention component. By consequence, Transformers cannot be trained on really long sequences because resource requirements are just too high. BERT, for example, sets a maximum sequence length of 512 characters.
Learn how large language models are working and how you can train open source ones yourself.
Keras is a high-level API for TensorFlow. It is one of the most popular deep learning frameworks.
Read about the fundamentals of machine learning, deep learning and artificial intelligence.
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