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Related Questions
- How does the trade-off between vocabulary size and model complexity affect the performance on rare or unseen words?
- Can you explain how the model handles out-of-vocabulary (OOV) words and how it impacts the overall performance?
- What techniques can be employed to improve the model's performance on tasks with rare or unseen words, such as subwording or character-level embeddings?
- How does the size of the word embeddings dictionary impact the model's ability to generalize to new, unseen words?
- Can you discuss the relationship between vocabulary size and the model's ability to learn semantic relationships between words?
- What are some common strategies for handling rare or unseen words in language models, and how do they impact the model's performance?
- How does the size of the word embeddings dictionary affect the model's performance on tasks that require understanding of nuanced language, such as idioms or figurative language?
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