Python transformers.optimization.AdamW is an implementation of the AdamW algorithm for optimizing the parameters of a deep learning model in the Transformers library. AdamW is an extension of the popular Adam optimizer, with the added benefit of employing weight decay to prevent overfitting. It computes adaptive learning rates for different parameters and updates them during training. This optimization technique helps improve the performance and generalization of the model by effectively updating the parameters based on their estimated importance.
Python AdamW - 30 examples found. These are the top rated real world Python examples of transformers.optimization.AdamW extracted from open source projects. You can rate examples to help us improve the quality of examples.