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Answer: Use a pre-trained model as the base for the custom model and fine-tune it with a dataset of domain-specific text.
Option A is the correct approach as using a pre-trained model as the base can provide a good starting point for the custom model. Fine-tuning the model with a dataset of domain-specific text can help the model learn the specialized vocabulary and technical jargon of the domain. Option B, training the model from scratch, may not be as effective due to the lack of pre-existing knowledge. Option C, combining multiple pre-trained models, may not necessarily improve the accuracy and can be more complex to implement. Option D, using a rule-based approach, may not be feasible for handling the large amount of technical jargon and specialized vocabulary.
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You are working on a project to implement a custom translation model for a specific domain using Azure AI Translator service. The domain has a large amount of technical jargon and specialized vocabulary. Which approach would you take to improve the accuracy of the custom translation model?
A
Use a pre-trained model as the base for the custom model and fine-tune it with a dataset of domain-specific text.
B
Train the custom model from scratch using a large dataset of domain-specific text.
C
Combine multiple pre-trained models and fine-tune them with a dataset of domain-specific text.
D
Use a rule-based approach to handle the technical jargon and specialized vocabulary.