In this step, we create our first model. You can either use promptranker to help you find the best labels and hypotheses or skip this step with your preferred setting.
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As a brief reminder, when defining the text classification task, we need to associate a label text (the label description) with the label name (the actual label) that we are intending to categorise. This label text, also referred sometimes as hypothesis or prompt, provides a semantic context to the model. |
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| title | If you want to continue using Promptranker: |
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This will run an unsupervised statistical Bayesian process that will end up outputting a reliability score of the best combination from the provided ones. It is recommended to use that combination for your model, both at zero and few-shot level. |
Enable ‘Rank label texts’ Provide label names, e.g. , For each label name, provide a list of alternative descriptions. For example, for positive, you can provide and for negative,
Provide hypothesis variations, e.g. use an empty one and add several different variations such as , | Status |
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| title | The author expresses a {} sentiment |
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 The result is a table where each variation is ranked according to a calculated score. The higher the score, the more likely the given labels and hypothesis yield good results in a real scenario.  4. Click Create and the model will be automatically created with the prompts (labels and hypothesis) that scored the top. |
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