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https://aws.amazon.com/blogs/machine-learning/hyperparameter-optimization-for-fine-tuning-pre-trained-transformer-models-from-hugging-face

Hyperparameter optimization for fine-tuning pre-trained transformer models from Hugging Face | Amazon Web Services

Large attention-based transformer models have obtained massive gains on natural language processing (NLP). However, training these gigantic networks from scratch requires a tremendous amount of data and compute. For smaller NLP datasets, a simple yet effective strategy is to use a pre-trained transformer, usually trained in an unsupervised fashion on very large datasets, and fine-tune […]



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Hyperparameter optimization for fine-tuning pre-trained transformer models from Hugging Face | Amazon Web Services

https://aws.amazon.com/blogs/machine-learning/hyperparameter-optimization-for-fine-tuning-pre-trained-transformer-models-from-hugging-face

Large attention-based transformer models have obtained massive gains on natural language processing (NLP). However, training these gigantic networks from scratch requires a tremendous amount of data and compute. For smaller NLP datasets, a simple yet effective strategy is to use a pre-trained transformer, usually trained in an unsupervised fashion on very large datasets, and fine-tune […]



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https://aws.amazon.com/blogs/machine-learning/hyperparameter-optimization-for-fine-tuning-pre-trained-transformer-models-from-hugging-face

Hyperparameter optimization for fine-tuning pre-trained transformer models from Hugging Face | Amazon Web Services

Large attention-based transformer models have obtained massive gains on natural language processing (NLP). However, training these gigantic networks from scratch requires a tremendous amount of data and compute. For smaller NLP datasets, a simple yet effective strategy is to use a pre-trained transformer, usually trained in an unsupervised fashion on very large datasets, and fine-tune […]

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      Large attention-based transformer models have obtained massive gains on natural language processing (NLP). However, training these gigantic networks from scratch requires a tremendous amount of data and compute. For smaller NLP datasets, a simple yet effective strategy is to use a pre-trained transformer, usually trained in an unsupervised fashion on very large datasets, and fine-tune […]
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