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Related Questions
- What are the key hyperparameters to adjust when fine-tuning a pre-trained language model for a specific task?
- How do learning rate, batch size, and number of epochs impact the performance of a fine-tuned language model?
- What is the effect of warm-up and learning rate scheduling on the convergence of the model during fine-tuning?
- How does the choice of optimizer, such as Adam or SGD, influence the model's performance and stability?
- What is the impact of regularization techniques, like dropout and L1/L2 regularization, on the model's overfitting and generalization?
- How does the size of the fine-tuning dataset and the number of training samples per class affect the model's performance?
- What is the effect of using a different pre-trained model architecture, such as BERT or RoBERTa, on the fine-tuned model's performance?
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