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Related Questions
- What are the primary goals of pre-training and fine-tuning in machine learning models for entity recognition tasks?
- How does the pre-training process prepare a model for entity recognition tasks, and what are the key steps involved?
- What are the advantages of pre-training over fine-tuning, and when should each approach be used?
- What types of data are typically used for pre-training and fine-tuning in entity recognition tasks, and how do they contribute to model performance?
- How does the fine-tuning process adapt a pre-trained model to a specific entity recognition task, and what are the key considerations for this process?
- What are some common challenges associated with fine-tuning, and how can they be addressed to improve model performance?
- Can you provide an example of a scenario where pre-training and fine-tuning are used together to achieve state-of-the-art performance in entity recognition tasks?
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