This blog post serves as the conclusion to a series on training BERT from scratch. It discusses the significance of BERT in Natural Language Processing, reviews the previous parts of the series, and outlines the process of building and training a BERT model. The post emphasizes understanding the model’s inner workings and shares insights on key components of the training process.
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The Final Frontier: Building and Training Your BERT Model
Introduction
This series explores the practical aspects of training BERT, a revolutionary model in Natural Language Processing, from scratch. We’ve covered the core concepts, tokenizers, dataset preparation, and now, in this final act, we’ll delve into the training process.
The Training Loop
In the training loop, we use modern frameworks like PyTorch and TensorFlow to build and train a deep neural network. We’ll leverage the Hugging Face transformers library to simplify the training process, allowing us to focus on other aspects of the code.
Accuracy
To evaluate our model’s performance during training, we’ll focus on two key metrics: loss and accuracy. We’ll measure the model’s accuracy in tasks such as Masked Language Modeling (MLM) and Next Sentence Prediction (NSP) to gauge its performance.
Training
With the dataset prepared, we’ll proceed to instantiate the BERT model and launch the training script using the Hugging Face Trainer object. Experimentation with hyperparameters can lead to enhanced performance, even when building a model from scratch.
Conclusion
After this journey, you’ll be equipped to apply this knowledge to your own activities and expand it further. The practical insights gained from training BERT from scratch can redefine your approach to AI and machine learning.
About the Author
Dimitris Poulopoulos is a machine learning engineer at HPE, with a track record of designing and implementing AI and software solutions for major clients. Follow him on Medium, LinkedIn, or Twitter for more insights into machine learning, deep learning, data science, and DataOps.
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