What are the best practices for fine-tuning a Transformer model with custom data

Question

I am fine-tuning a transformer model for a specific domain like finance, How can I leverage pre-trained models to enhance domain-specific accuracy without losing general language capabilities?

Answer 1

Pre-trained models can be leveraged for fine-tuning while preserving their general language capabilities in various domains such as finance etc. This approach offers a unique set of best practices:

• Select a strong base model: You can start with a pre-trained language model known for robust general language understanding, such as GPT or BERT.
• Domain-specific Fine-Tuning: If you have a selected domain for which you are fine-tuning a model, such as finance, then use a high-quality finance-specific dataset that includes various document types such as financial reports, articles, and industry-specific jargon.
• Layer-freezing strategy: You should freeze the lower layer of the pre-trained model during the initial training phase to retain general language knowledge and fine-tune only the higher layer with your domain data.
• Gradual Unfreezing: Implement a gradual unfreezing technique that incrementally unfreezes layers and fine-tunes deeper ones to balance general language retention with doing-specific adaptation.
• Regularization and warm-up: Use techniques like learning rate warm-up and regularization, such as dropout, to stabilize training and prevent overfitting domain data.

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