Few-shot learning refers to the model’s ability toclassify new data when only a limited number of training instances (e.g., 10 to 100) have been provided. As a result, after being exposed to a small amount of prior information, the model improves its performance.
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Few-shot learning and active learning combined together are known as active few-shot learning (FASL). FASL aims at tackling the challenge of quickly developing and deploying new models for real-world problems with the least effort.
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Advantages
The Few-shot model has multiple advantages including:
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✅ Increased model quality. Due to the combination of technology, in multiple use cases FASL surpasses the zero-shot model performance and allows you to reach a very high quality of your model.
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Key elements
To create any few-shot model using Symanto Brain, there are two key elements required:
Classes or also called labels, between which to discriminate (classify) the text, e.g.
Status colour Red title NEGATIVE Status colour Green title POSITIVE Patterns, also known as label descriptors, allow the semantic matching between the analysed text and the different labels, e.g.
This text is {}
Input: The product has no issues but the packaging causes so much extra to squirt out and you can't stop it. For how expensive it is it's such a waste.
Result:
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Examples
Please head over to Use Case Examples to view some common use cases.
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