We used the annotated snippets to train a random forest model with 200 estimators or trees. The random forest model maximum depth was set to 15, with maximum features automatically determined by the model and the gini split criterion. Hyperparameters were determined through gridsearch and other testing. We split the 1856 text snippets into 85% for training and 15% for testing. The model performance was measured by accuracy.

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