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Related Questions
- What are some regularization techniques that can be applied to pre-trained language models to prevent overfitting on small datasets?
- How can data augmentation techniques be used to increase the size and diversity of the training dataset for entity categorization tasks?
- What are some strategies for early stopping and learning rate scheduling to prevent overfitting during fine-tuning?
- Can you explain the concept of dropout and its application in preventing overfitting in pre-trained language models?
- How can transfer learning be used to leverage knowledge from pre-trained models and adapt it to the specific task of entity categorization?
- What are some techniques for ensemble methods, such as bagging and boosting, to combine the predictions of multiple models and reduce overfitting?
- Can you discuss the role of hyperparameter tuning in preventing overfitting, and how to use techniques such as grid search and random search to optimize model performance?
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