Multi-label text classification / cheat sheet
Use this cheat sheet
If the text samples in your dataset can belong to multiple classes.
Example use case
Text emotions |
A piece of text can be both positive and caring. |
Data preparation
Structure of imported csv
Datasets with text for text classification need to include:
-
1 column with text to be used as input
-
1 one-hot encoded column for each Feature, also named label, used as target. That is, either the text sample contains the label or not.
Example:
Each row contains a 1 if the label is present in the text sample and a 0 if it is not.
| Text sample | Label 1 | Label 2 | Label 3 |
|---|---|---|---|
Lorem ipsum dolor sit amet, consectetur adipiscing elit. |
0 |
1 |
1 |
Uis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. |
1 |
1 |
0 |
Dataset view
In the Datasets view, make sure that:
Create feature set
Create a Feature set with all the label columns to use as the target in your model.
Modeling
Use the Experiment wizard to build a model.
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Select the Text feature as Input.
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Select the Feature set as Target.
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Select Multi-label text classification as Problem type.
Run experiment
When you’ve created an experiment with the Experiment wizard, a ready-to-run model will populate the Modeling canvas.
The only thing left to do is to click Run. You can change the settings but yiu don’t have to.
Evaluate experiment
In the Evaluation view, you can see how the loss gets lower for each epoch.
Predictions inspection
By default, the predictions from the validation subset at the best epoch are shown.
Select the class Label that you want to inspect. The prediction table, the Confusion matrix, and the ROC Curve will show the performance of the model on the selected Label.
The threshold value allows you to control how the errors made by the model distribute between false positive and false negative.
Deployment
Click New deployment. Select experiment and epoch for deployment.
Click Enable to make the deployment accessible through the Peltarion Deployment API for forward pass queries.
Text classifier web app
To make it easier to test your model, we have provided a web app.
Click Open web app and test your deployed model.
