The following notebooks demonstrate how to construct more advanced models, including models for multiple annotation tasks, models with multiple vectorizers and classifiers, and models with bootstrap resampling.
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Model with Multiple Vectorizers - This notebook demonstrates how to construct a model for a single annotation task using multiple vectorizers. This can be useful if you want to combine different types of vectorizers (e.g. pretrained-embedding models, count-based models)
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Model with Multiple Classifiers - This notebook demonstrates how to construct a model for a single annotation task using multiple classifiers. This can be useful if you want to combine different types of classifiers, and compare their performance on the same data.
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Model with Multiple Annotations - This notebook demonstrates how to run the model fitting process for a multiple annotations.
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Model with Bootstrap - This notebook demonstrates how to run the model fitting process with bootstrap resampling.