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TL;DR:
TL;DR: Hyperparameter tuning of HuggingFace models with AWS Sagemaker is a powerful way to maximize model performance for a specific task. This post demonstrates how to use the HuggingFace Estimator and Sagemaker Tuner to tune DistilBERT for sentiment classification on the tweet_eval dataset. The optimal hyperparameters are learning rate = 0.000175, optimizer = Adafactor, warmup_steps = 192 and weight decay = 0.000111. Following this, the model can be deployed and predictions can be made. Notebooks and scripts are available online.
Summary:
wrote it Introduction Deep learning frameworks and cloud providers are making it easier than ever for practitioners to optimize their models using hyperparameter tuning. The HuggingFace Estimator and the Amazon Web Service’s Sagemaker Tuner provide an easy-to-use interface for performing such optimizations. In this blog post, I’ll be discussing how to use the HuggingFace Estimator and the Sagemaker Tuner to optimize deep neural networks. Data Preparation In order to fine-tune a HuggingFace transformer with the Sagemaker Tuner, we need to first acquire the dataset we intend to use for our task. For this example, I will be using the tweet_eval dataset, which is available under a Creative Commons Attribution 3.0 Unported License. After downloading the dataset, we will need to tokenize and process it and then convert it to tensors. We can use the HuggingFace datasets library to easily save the data to an S3 bucket, where our training job can access it. Hyperparameter Settings Before running a tuning job, we want to think about the hyperparameters we want to optimize and the range of values we think might be appropriate. Common hyperparameters that get