We are focused on developing encoder-based models, we find encoder models to be extremely efficient for information extraction (IE) tasks, where contextual understanding and flexibility make this approach for IE a gold choice. We've got it covered in our blog post.

Models were trained to work in zero-shot and few-shot learning settings, so they could be efficiently fine-tuned with minimal data. It refers to the ability of ML models to learn from a minimal set of data. Unlike traditional ML models that require large datasets to learn effectively, few-shot learning models are designed to make accurate predictions with fewer examples. Read more in our blog post. Each model is capable of performing vast information extraction types, and depending on your final use case different model would be a better fit.


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