The `AdamW.step` method is a function in the `optimization` module of the Python Transformers library. It is used to update the parameters of a neural network model using the AdamW optimizer. This method performs a single optimization step, adjusting the weights and biases of the model based on the gradients calculated during the backpropagation process. The AdamW optimizer combines the Adam optimizer with weight decay, which helps prevent overfitting. By calling the `step` method, the model's parameters are updated with the calculated gradients, allowing the model to learn and improve its performance over time.
Python AdamW.step - 30 examples found. These are the top rated real world Python examples of transformers.optimization.AdamW.step extracted from open source projects. You can rate examples to help us improve the quality of examples.