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Related Questions
- What are the primary differences in training data for domain-specific NLP models compared to general-purpose models?
- How do domain-specific NLP models handle out-of-vocabulary words and domain-specific terminology?
- Can you explain the concept of 'domain adaptation' in the context of NLP models?
- How does the quality and quantity of training data impact the performance of domain-specific NLP models?
- Are domain-specific NLP models more prone to overfitting due to the smaller and more specialized training data?
- Can general-purpose NLP models be fine-tuned for a specific domain, or is it better to start from scratch with a domain-specific model?
- What role does data curation and preprocessing play in preparing training data for domain-specific NLP models?
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