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Related Questions
- Can data augmentation techniques be prone to overfitting and underfitting in certain scenarios?
- How can overfitting and underfitting impact the performance of a semantic preserving technique in NLP?
- Are there any common pitfalls when using data augmentation with semantic preserving techniques that can lead to suboptimal results?
- How can the quality of the augmented data affect the performance of a model trained with semantic preserving techniques?
- Can the type of data augmentation technique used influence the likelihood of overfitting or underfitting?
- Are there any strategies to prevent or mitigate overfitting and underfitting when using data augmentation with semantic preserving techniques?
- How can the evaluation metrics used to assess the performance of a model trained with semantic preserving techniques impact the detection of overfitting or underfitting?
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