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Fine Tuning is a cost-efficient way of preparing a model for specialized tasks. Fine-tuning reduces required training time as well as training datasets. We have open-source pre-trained models. Hence, we do not need to perform full training every time we create a model.
This project aims to compare 3 methods for transforming unstructured textual content from Hebrew legal documents into structured data