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Related Questions
- How does the size of the pre-training dataset impact the performance of BERT and RoBERTa models on handling rare or unseen words in downstream NLP tasks?
- Can larger pre-training datasets improve the ability of BERT and RoBERTa models to generalize to new or out-of-vocabulary words in downstream applications?
- What are the trade-offs between using larger pre-training datasets and the risk of overfitting in BERT and RoBERTa models when it comes to handling out-of-vocabulary words?
- How do BERT and RoBERTa models handle out-of-vocabulary words in downstream tasks, and can larger pre-training datasets improve their performance in this regard?
- Can BERT and RoBERTa models trained on larger pre-training datasets learn to adapt to new words or concepts that are not present in their training data?
- What is the relationship between pre-training dataset size and the ability of BERT and RoBERTa models to handle out-of-vocabulary words in downstream natural language processing tasks?
- How do larger pre-training datasets impact the ability of BERT and RoBERTa models to generalize to new domains or tasks that require handling out-of-vocabulary words?
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