Disadvantages of the Transformer model

The Transformer model has these major flaws:

The Transformer model requires a lot of calculations during the training process, especially when processing large data sets and long sequences. Therefore, using Transformer models in real-time applications or resource-constrained devices is challenging.

2. Difficulty in parallelization: The sequential nature of the Transformer model may make it difficult to parallelize the training process, thus slowing down training time.

One of the disadvantages of the Transformer model is the lack of interpretability. Compared to some other machine learning models, the Transformer model does not have an explicit input-output mapping, which makes it more difficult to explain its inner workings.

Transformer models are sensitive to hyperparameters, and tuning hyperparameters for optimal performance is more challenging.

5. Limited input length: Transformer models are often limited by the length of the input sequence they can process, which is a problem for tasks that require longer context.

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