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Related Questions
- What are the common strategies for handling out-of-vocabulary words in transformer models?
- How do transformer models typically handle words that are not present in their training data?
- What is the impact of out-of-vocabulary words on the performance of transformer models?
- Can transformer models learn to generate out-of-vocabulary words, or do they always fail?
- How do different pre-training objectives, such as masked language modeling or next sentence prediction, affect the handling of out-of-vocabulary words?
- What techniques can be used to fine-tune transformer models for better handling of out-of-vocabulary words?
- How do transformer models compare to other language models, such as recurrent neural networks, in terms of handling out-of-vocabulary words?
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