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Related Questions
- What are some strategies for handling out-of-vocabulary words when using contextualized embeddings in models?
- How can models be designed to handle sparse data when using contextualized embeddings?
- What are some techniques for fine-tuning contextualized embeddings for specific tasks?
- How do contextualized embeddings compare to traditional word embeddings in terms of performance and interpretability?
- Can you provide examples of models that use contextualized embeddings for tasks such as sentiment analysis and question answering?
- What are some challenges associated with using contextualized embeddings in models, and how can they be addressed?
- How can contextualized embeddings be used to improve the performance of models on tasks that involve complex relationships between words?
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