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Related Questions
- How do transformer models typically handle rare words in terms of embedding representation?
- What are some common strategies for out-of-vocabulary (OOV) word handling in transformer models?
- How do subwordization and tokenization techniques address rare word and OOV issues in transformer models?
- Can you explain the difference between word-piece models and character-based models in handling rare words and OOVs?
- How does the vocabulary size of a transformer model impact its performance on rare words and OOVs?
- Are there any hybrid approaches that combine different OOV handling techniques in transformer models?
- What are some best practices for configuring OOV handling in transformer models for optimal performance?
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