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Related Questions
- What are the key differences between contextualized and non-contextualized language models in text classification tasks?
- How do contextualized language models handle out-of-vocabulary words and rare entities in text classification tasks?
- Can you provide examples of how contextualized language models can improve text classification accuracy in sentiment analysis and topic modeling tasks?
- How do contextualized language models leverage contextual information to improve text classification performance in tasks such as named entity recognition and intent detection?
- What are the computational requirements and training time complexities of contextualized language models compared to non-contextualized models in text classification tasks?
- Can contextualized language models be fine-tuned for specific text classification tasks, and if so, what are the benefits and challenges of doing so?
- How do contextualized language models handle linguistic phenomena such as word embeddings, polysemy, and figurative language in text classification tasks?
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