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Related Questions
- What is the primary purpose of metrics like accuracy and perplexity in evaluating large language models like Llama and Qwen?
- How do F1 score and accuracy differ in evaluating models' performance, and what are their strengths and limitations?
- Can perplexity be a more suitable metric than accuracy for certain tasks, such as question-answering, and why?
- What are the implications of using F1 score for evaluating models like Llama and Qwen, especially in cases of class imbalance?
- How can metrics like perplexity and F1 score be combined to provide a more comprehensive evaluation of models' performance?
- What role do metrics play in hyperparameter tuning, and how can they influence the overall performance of Llama and Qwen?
- Can metrics be used to compare the performance of models across different datasets, and what are the challenges involved in doing so?
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