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Related Questions
- What are the key benefits of using contextual embeddings over traditional word embeddings?
- How do contextual embeddings capture nuances of language beyond word meaning?
- Can you explain the concept of contextualized word representations in machine learning?
- What are some common applications of contextual embeddings in natural language processing?
- How do contextual embeddings handle out-of-vocabulary words?
- What are the challenges in training contextual embeddings models?
- Can you provide examples of popular contextual embeddings models and their architectures?
- How do contextual embeddings improve the performance of downstream NLP tasks?
- What is the relationship between contextual embeddings and attention mechanisms in NLP?
- Can you discuss the trade-offs between contextual embeddings and traditional word embeddings?
- How do contextual embeddings handle polysemy and homophones?
- What is the role of pre-training in contextual embeddings models?
- Can you explain the concept of contextualization in language models and its impact on downstream tasks?
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