Zero-shot learning refers to the model’s ability to classify objects that it 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.
The Zero-shot models have This approach is also called zero-shot learning.
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Advantages
There are multiple advantages, including:
✅ No need to be fine-tuned. Zero-shot models are one of the fastest ways for training data. Fast way to classify text without taking up your time for additional annotation of examples, solving the problem of “cold start”.
✅ Unlimited possibilities for labels. As in general zero-shot models work without any training data from you, your 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. Zero-shot models allow . 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.
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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.
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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With this, the zero-shot model allows rapid classification of the data set without the need for prior training while getting and with results similar to those of a deep learning model trained with large amounts of data.
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Examples
Please head over to Use Case Examples to view some common use cases.
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Next: How to
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