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Related Questions
- What are contextual embeddings and how are they different from traditional word embeddings?
- How do contextual embeddings capture the nuances of language and improve language model performance?
- Can you provide an example of a language model that uses contextual embeddings and explain its architecture?
- How do contextual embeddings handle out-of-vocabulary words and words with multiple meanings?
- What are some common applications of contextual embeddings in natural language processing tasks?
- How do contextual embeddings compare to other types of embeddings, such as word2vec and GloVe?
- Can you explain the impact of contextual embeddings on downstream tasks, such as sentiment analysis and question answering?
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