What are the common hyperparameters in NLP models?

What are the common hyperparameters in NLP models?

Common hyperparameters in NLP models include learning rate, batch size, number of layers, and dropout rate.

How does the learning rate affect model performance?

The learning rate controls how much to change the model in response to the estimated error each time the model weights are updated. A high learning rate can lead to convergence issues, while a low learning rate may result in a long training time.

What is the impact of batch size on training?

Batch size affects the stability of the training process. Smaller batch sizes can lead to noisy gradient estimates, while larger batch sizes provide more accurate estimates but require more memory.

Can you explain dropout rate?

Dropout rate is a regularization technique used to prevent overfitting by randomly setting a fraction of the input units to zero during training.