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Related Questions
- What are some key differences between transformer-based models and recurrent neural networks (RNNs) in NLP tasks?
- Can transformer-based models handle tasks that require sequential dependencies, such as language modeling and machine translation?
- How do transformer-based models handle out-of-vocabulary words and rare words in NLP tasks?
- What are some common applications of transformer-based models in text classification tasks, such as sentiment analysis and topic modeling?
- Can transformer-based models be used for tasks that require attention to specific parts of the input, such as question answering and text summarization?
- How do transformer-based models compare to other NLP architectures, such as CNNs and RNNs, in terms of performance and efficiency?
- What are some challenges and limitations of using transformer-based models in NLP tasks, and how can they be addressed?
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