With a proper set of hypotheses labels describing the labels of the classification task, the model can infer by semantic similarity how to categorize a text sequence without having been explicitly trained for that task. For example, a sentiment analysis task could be formatted as follows:
...
For the ease of use, we can create pattern templates with a curly-bracket placeholder for replacing the corresponding label each time. In the example above, it would have been This person expresses a {} sentiment.
Parameters
Parameter | Description |
|---|---|
model | The language model to use. Call GET /zero-shot/ for the models available. Choose symanto_brain_multilingual only if there is no specific language model for your selected languauge. |
all | If true only returns the most probable label. Otherwise it returns all of them. |
multi_label | If false, returned probability distribution among provided labels for each instance will sum up to 1. Otherwise it will be unnormalized. |
Examples
Please head over to Use Case Examples to view some common use cases and their configurations.