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Related Questions
- What are the key performance metrics used to evaluate pre-trained language models in NLP tasks?
- How do evaluation strategies for pre-trained language models differ from those used for custom knowledge graphs in NLP tasks?
- What are the advantages and disadvantages of using pre-trained language models versus custom knowledge graphs in NLP tasks?
- How do pre-trained language models handle out-of-vocabulary words and domain-specific terminology compared to custom knowledge graphs?
- What are some common evaluation metrics used for pre-trained language models in NLP tasks, such as perplexity, accuracy, and F1-score?
- How do custom knowledge graphs differ from pre-trained language models in terms of knowledge representation and retrieval?
- What are some challenges and limitations of using pre-trained language models in NLP tasks, such as handling ambiguity and uncertainty?
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