In this step, we create our first model. You can either use promptranker to help you find the best labels and hypothesis hypotheses or skip this step with your preferred setting.
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API Docs: https://api.symanto.net/active-learning/docs#/v2/create_model_v2__post |
Set up your model
Select
from the side navigationStatus title Create New Select your dataset from the dropdown
Select the language of the text
Choose a model type
symanto_fast (for demonstration purposes, live demo, etc.)
symanto (for actual model training with a purpose)
Select or unselectStatus
Select the multi_label option (select it if the text classification task allows the prediction of multiple labels per single text instance. Otherwise, the task is considered as multi-class (or binary if only 2 labels are categorized). Select the language of the textcolour Blue title Optional Choose an embedding model
Choose symanto_brain_multilingual only if there is no specific language model for your selected langauge
language
Add a model name or keep the automatically assigned oneStatus colour Blue title Optional
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Define your labels
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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 categorizecategorise. This label text, also referred sometimes as hypothesis or prompt, provides a semantic context to the model. |
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Expland the 'Check or modify labels '
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2. Select the number of labels you want to have using the slider
3. Continue with/ without using Promptranker
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3. Provide label names, e.g.
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label texts (hypotheses), e.g.
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label name in the label text too, such as
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2.
 2.1. Use the slider to select up to 10 examples. 2.2. Change the label dropdown to see examples for |
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each of your labels. 2.3. Change the assigned label if necessary 3. Click |
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Enable “Inspect typical examples” to see how your model performs
Use the slider to select up to 10 examples.
Change the label dropdown to see examples for every of your labels.
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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. |
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 4. Click |
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Next:
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Train
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