Text classification model is useful for text classification problems such as sentiment analysis or domain detection for dialogue systems. Provided model here is trained to classify the given query into 1 of 4 domains described below to use it as an initial step in the interactive weather chat bot, which was presented in GTC 2020 keynote.
Text Classification Model can be used for domain classification as the first step in the dialogue systems, to route query according to the appropriate domain. This classification is a task specific according to the domains and examples provided in the training data. Usually in practical settings you need to take this model (pretrained Bert model) and train it on you own dataset.
Our text classification model uses a pretrained BERT model (or other BERT-like models) followed by a classification layer on the output of the first token ([CLS]).
We used a proprietary data set that was collected via Mechanical Turk to describe large variety of queries that fall in one of the next 4 domains:
Training dataset included 2150 example of queries divided for 4 domains described above. We got around 95% domain classification accuracy for this data.
These model checkpoints are intended to be used with Nvidia Riva.
Note: The model is encrypted and will only operate with the model load key
By downloading and using the models and resources packaged with Nvidia Riva, you would be accepting the terms of the Riva license
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