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Related Questions
- How does the inclusion of rare or specialized words impact the overall performance of a language model?
- Can you explain the concept of out-of-vocabulary (OOV) words and how it relates to rare or specialized words in a model's training data?
- What are some strategies for handling rare or specialized words in a model's training data to balance the trade-off between performance and computational resources?
- How does the distribution of rare or specialized words in a dataset affect the model's ability to generalize to unseen data?
- Can you discuss the role of subword modeling in handling rare or specialized words and its implications on model size and training time?
- What are the potential consequences of not including rare or specialized words in a model's training data, and how can it impact the model's performance in real-world applications?
- Are there any techniques or algorithms that can efficiently handle rare or specialized words in a model's training data without significantly increasing training time and computational resources?
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