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Related Questions
- How does the model handle out-of-vocabulary (OOV) words when it encounters them in real-world data?
- Can the model's performance be significantly impacted if it encounters a large number of OOV words during training or deployment?
- Are there any techniques or strategies that the model can use to learn or adapt to OOV words, and how might these impact its overall performance?
- Can the model's ability to handle OOV words affect its ability to generalize to new or unseen data, and if so, why?
- In what ways might the model's performance be impacted if it is unable to effectively handle OOV words?
- Are there any trade-offs between improving the model's ability to handle OOV words and other aspects of its performance, such as accuracy or efficiency?
- How might the model's ability to handle OOV words impact its performance in tasks that involve handling out-of-domain or out-of-context data?
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