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Related Questions
- What is the primary advantage of using encoder-decoder architecture in sequence-to-sequence tasks compared to traditional NLP models?
- Can you provide a scenario where a pre-trained model can be fine-tuned for a specific task, such as language translation?
- How does fine-tuning a pre-trained model with a target dataset improve the accuracy of the encoder-decoder architecture in sequence-to-sequence tasks?
- What types of tasks are best suited for encoder-decoder architecture, and why?
- Can you provide an example of a sequence-to-sequence task where a traditional NLP model would struggle, but an encoder-decoder architecture would excel?
- How does the encoder-decoder architecture handle out-of-vocabulary words or rare words in a sequence-to-sequence task?
- Can you provide an example of a real-world application of encoder-decoder architecture in a sequence-to-sequence task, such as machine translation or text summarization?
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