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Related Questions
- What are the different types of word embeddings and their characteristics?
- How do the choice of embedding size and dimensionality impact the model's performance?
- Can you explain the concept of contextualized word embeddings and their use in language models?
- How does the choice of pre-trained word embeddings affect the training time and model accuracy?
- What are the trade-offs between using static and dynamic word embeddings in language models?
- How do the choice of word embeddings impact the model's ability to generalize to out-of-vocabulary words?
- Can you discuss the role of word embeddings in capturing semantic relationships between words in a language model?
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