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Related Questions
- What is the primary difference between contextual embedding and traditional word embedding in text generation models?
- How does contextual embedding help improve the performance of language models in downstream tasks such as sentiment analysis and question answering?
- Can you provide an example of a pre-trained language model that uses contextual embedding, and how it is fine-tuned for a specific task?
- What are some common challenges associated with pre-training and fine-tuning language models with contextual embedding?
- How does the quality of the pre-training data impact the effectiveness of contextual embedding in text generation models?
- Can contextual embedding be used in combination with other techniques such as attention mechanisms and convolutional neural networks?
- What are some potential applications of contextual embedding in areas such as natural language processing, information retrieval, and text summarization?
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