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.
...
Few-shot learning and active learning combined together are known as active few-shot learning (FASL). FASL . Active few-shot learning aims at tackling the challenge of quickly developing and deploying new models for real-world problems with the least effort.
...
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:
| Status | ||||
|---|---|---|---|---|
|
...