Deberta-based Daily Dialog style dialog-act annotations classification model. It takes two sentences as inputs (one previous and one current of a dialog). The previous sentence can be an empty string if this is the first utterance of a speaker in a dialog. Outputs one of four labels (exactly as in the daily-dialog dataset ): dummy (0), inform (1), question (2), directive (3), commissive (4)
from simpletransformers.classification import ( ClassificationModel, ClassificationArgs ) model = ClassificationModel("deberta", "diwank/dyda-deberta-pair") convert_to_label = lambda n: ["__dummy__ (0), inform (1), question (2), directive (3), commissive (4)".split(', ')[i] for i in n] predictions, raw_outputs = model.predict([["Say what is the meaning of life?", "I dont know"]]) convert_to_label(predictions) # inform (1)
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