The general idea is to classify texts that the model has never seen before, or in other words, allow us to assign an appropriate label to a piece of text without having received any training examples before. This approach is also called zero-shot learning.
Advantages
There are multiple advantages, including:
✅ No need to be fine-tuned. Fast way to classify text without taking up your time for additional annotation of examples.
✅ Unlimited possibilities for labels. Your model will be able to detect any label you might be interested in finding in your data, such as emotions, different topics, sentiment, personality traits and others.
✅ High-performance quality. Rapid classification of the data set without the need for prior training while getting results similar to those of a deep learning model trained with large amounts of data.
Key elements
To create any model using Symanto Brain, there are two key elements required:
Classes or also called labels, between which to discriminate (classify) the text, e.g. NEGATIVE 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: THIS TEXT IS NEGATIVE
With this, the zero-shot model allows rapid classification of the data set without the need for prior training and with results similar to those of a deep learning model trained with large amounts of data.
Examples
Please head over to Use Case Examples to view some common use cases.