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Related Questions
- How does increasing the number of attention heads affect the model's capacity to learn complex relationships between input elements?
- Can increasing the number of attention heads lead to an increase in model parameters, and if so, how does this impact model training and inference?
- What are the trade-offs between increasing the number of attention heads and increasing the embedding size or hidden size in a transformer model?
- How does the number of attention heads interact with other model parameters, such as the number of encoder and decoder layers, to impact model performance?
- Are there any empirical studies that have investigated the relationship between the number of attention heads and other model parameters?
- Can increasing the number of attention heads lead to overfitting or underfitting, and if so, how can this be mitigated?
- How does the choice of number of attention heads impact the model's ability to learn long-range dependencies and contextual relationships in the input data?
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