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Simplify using the HuggingFace trainer object
HuggingFace serves as a home for many popular open source NLP models. Many of these models are effective out of the box, but often require some training or fine-tuning to improve performance for specific use cases. As the LLM collapse continues, this article takes a step back and revisits some of the core building blocks provided by HuggingFace that simplify training NLP models.
Traditionally, NLP models can be trained using standard PyTorch, TensorFlow/Keras, and other popular ML frameworks. You can also use this method, but it requires a deeper understanding of the framework you are using and more code to create the training loop. HuggingFace’s Trainer class allows you to easily manipulate any NLP Transformers model you want.
Trainer is a class specifically optimized for Transformers models and also provides tight integration with other Transformers libraries such as Datasets and Evaluate. More advanced level trainers also support distributed training libraries and easily integrate with infrastructure platforms such as Amazon SageMaker.
In this example, we will see how to use the Trainer class locally to fine-tune a popular BERT model on the IMBD dataset for a text classification use case: citations for a large movie review dataset.
Note: This article assumes a basic knowledge of the fields of Python and NLP. I won’t go into specific machine learning theory regarding model construction or selection. This article is dedicated to understanding how to fine-tune the existing pre-trained models available in the HuggingFace Model Hub.
- setting
- Tweaking BERT
- Additional resources and conclusions
This example works in SageMaker Studio and leverages the conda_python3 kernel on a ml.g4dn.12xlarge instance. Note that you can use smaller instance types, but this may impact training speed depending on the number of CPUs/workers available.
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