Fine-tuning GPT model for extracting fields from Hebrew invoices

API
1

I am currently working on fine-tuning a GPT model to extract specific fields from Hebrew invoices. To do this, I am using an OCR service to retrieve the invoice text and then passing it to the model along with a JSON file containing the expected valid fields as the result.

Although I have trained the model using 1500 examples, the results are not as accurate as I had hoped. However, I have discovered that the GPT-3.5-turbo model provides much more accurate results with just a simple prompt and one example.

I’m wondering if I’m doing something wrong with my approach or if I need to train the model using more examples to improve its accuracy. Can anyone offer any advice or suggestions? Any help would be greatly appreciated.

Thank you in advance!

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Which models did you train for fine-tuning? Did you use davinci?

1,500 examples should be enough data to get 90%+ accuracy.