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Related Questions
- What are the key considerations when selecting a dataset for fine-tuning a dialogue model?
- Can you explain the difference between in-domain and out-of-domain fine-tuning, and when to use each approach?
- How can transfer learning be applied to adapt a pre-trained dialogue model to a new domain or application?
- What are some common techniques used to adapt a pre-trained dialogue model to a specific domain or application, such as label smoothing or knowledge distillation?
- How can data augmentation techniques be used to increase the size and diversity of the training data for a dialogue model?
- What are some best practices for fine-tuning a dialogue model for a specific domain or application, such as setting the learning rate, batch size, and number of epochs?
- Can you discuss the trade-offs between fine-tuning a pre-trained model versus training a model from scratch for a specific domain or application?
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