Fine-Tuning Embedding Models
FAQs
How much data is needed to fine-tune?
The amount of data depends on the use case, but generally, several thousand examples of similar content pairs are needed. Adding more examples will improve the quality of your fine-tuned model.
How long does fine-tuning take?
Depending on the amount of data and computational resources available, the process typically takes several hours to days.
Will fine-tuning affect the model's general language capabilities?
If done properly, fine-tuning significantly enhances domain-specific understanding while maintaining general language capabilities. However, overly narrow training can make the fine-tuned model too specialized.
How do I know if fine-tuning is working?
Test the model on sample content from your use case. Compare similarity scores and rankings of related content pairs before and after fine-tuning to measure improvement.
Is fine-tuning the right choice for my use case?
Fine-tuning is most beneficial when you have a large amount of domain-specific data that differs significantly from the model's base training data. Before deciding to fine-tune, consider factors such as the uniqueness and complexity of your content, the amount of labeled data available, and the potential benefits and improvements you expect from fine-tuning.