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Related Questions
- What are the primary differences in computational resources required for pre-trained and custom-trained language models in terms of memory and processing power?
- How does the size of the vocabulary and the complexity of the model architecture impact the computational resources required for pre-trained and custom-trained language models?
- What are the trade-offs between the size of the training dataset and the computational resources required for pre-trained and custom-trained language models
- How do custom-trained language models handle low-resource languages and how much computational resources are required?
- What are some techniques used to reduce computational resources required for custom-trained language models
- Can pre-trained language models be fine-tuned using smaller computational resources?
- What are the performance implications of using smaller model architectures or reduced computational resources on pre-trained and custom-trained language models?
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