Text classifier, based on the BERT and a Bayesian neural network, which can train on small labeled texts and doubt its decision
Text classifier, based on the BERT and a Bayesian neural network, which can train on small labeled texts and doubt its decision.
The goal of this project is developing of simple and power text classifier based on transfer learning and bayesian neural networks.
A transfer learning (particulary, well-known BERT model) helps to generate special contextual embeddings for text tokens, which provide a better discrimination ability in feature space, than classical word embeddings. Therefore we can use smaller labeled data for training of final classifier.
Bayesian neural network in final classifier models uncertainty in data, owing to this fact probabilities of recognized classes returned by this network are more fair, and bayesian neural network is more robust to overfitting.
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