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Related Questions
- How do BERT and RoBERTa algorithms handle out-of-vocabulary words and what are their limitations?
- In what ways do BERT and RoBERTa contextualize out-of-vocabulary words to improve common sense understanding?
- How do the pre-training and fine-tuning processes in BERT and RoBERTa impact their ability to handle out-of-vocabulary words?
- Can you explain the difference between BERT and RoBERTa's handling of out-of-vocabulary words in terms of their impact on common sense understanding?
- What are some scenarios where BERT and RoBERTa struggle with out-of-vocabulary words, and how can these issues be addressed?
- How do BERT and RoBERTa's reliance on pre-trained word embeddings affect their ability to generalize to out-of-vocabulary words?
- Can you discuss the trade-offs between using pre-trained models like BERT and RoBERTa versus training a model from scratch for specific out-of-vocabulary tasks?
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