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Related Questions
- How can I use active learning to select the most informative unlabeled data for my NLP model?
- What are some common techniques for leveraging weak supervision, such as using distant supervision or self-training, to improve NLP model performance?
- Can you explain the concept of pseudo-labeling and how it can be used to harness the power of unlabeled data for NLP model fine-tuning?
- How can I use transfer learning to adapt my NLP model to new domains or tasks by leveraging unlabeled data from those domains?
- What are some strategies for incorporating human feedback, such as user ratings or annotations, to improve the performance of my NLP model on unlabeled data?
- Can you discuss the trade-offs between using more labeled data versus more unlabeled data for NLP model training, and how to balance these trade-offs?
- How can I use self-training to leverage unlabeled data and improve the performance of my NLP model, especially in cases where labeled data is scarce?
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