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Related Questions
- Can you explain how overfitting occurs when a model encounters out-of-vocabulary (OOV) words or phrases?
- How does the model's performance degrade when it encounters OOV words or phrases, leading to overfitting?
- What are some strategies to mitigate overfitting when a model encounters OOV words or phrases?
- Can you discuss the impact of sparse data on overfitting when a model encounters OOV words or phrases?
- What role does the model's architecture and training data play in reducing overfitting when encountering OOV words or phrases?
- Can you describe the trade-off between overfitting and underfitting when a model encounters OOV words or phrases?
- What techniques can be used to prevent overfitting when a model is trained on a small dataset with many OOV words or phrases?
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