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Related Questions
- What are the key differences between contextualized embeddings and word embeddings?
- How do contextualized embeddings capture the nuances of language and improve the performance of text generation models?
- Can you provide examples of popular contextualized embedding models and their applications?
- How do contextualized embeddings handle out-of-vocabulary words and rare words?
- What are the challenges in training and fine-tuning contextualized embedding models?
- Can contextualized embeddings be used for sentiment analysis and topic modeling?
- How do contextualized embeddings compare to other techniques for improving text generation models, such as attention mechanisms and pre-training?
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