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Related Questions
- What are the most effective techniques for removing noisy or irrelevant data from a training dataset?
- How can data curation and filtering impact the performance of a text generation model?
- What are some common metrics used to evaluate the quality of a subset of training data?
- Can you explain the concept of data augmentation and how it can be used to expand a training dataset?
- What are some strategies for selecting a diverse and representative subset of training data?
- How can data filtering techniques such as tokenization, stopword removal, and stemming impact the performance of a text generation model?
- What are some common challenges associated with data curation and filtering in text generation models, and how can they be addressed?
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