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Related Questions
- What is the typical approach to handling out-of-vocabulary words during fine-tuning of pre-trained language models?
- How does the pre-trained model adapt to novel words or tokens that it has not seen during training?
- What strategies can be employed to improve the model's performance on out-of-vocabulary words?
- Can you explain the concept of subwording and its role in handling out-of-vocabulary words?
- How does the model's vocabulary size affect its ability to handle out-of-vocabulary words?
- What is the relationship between the pre-trained model's vocabulary and its performance on out-of-vocabulary words?
- Are there any specific techniques or architectures that are better suited for handling out-of-vocabulary words during fine-tuning?
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