In what follows, I'll show how to fine-tune a BERT classifier, using Huggingface and Keras+Tensorflow, for dealing with two different text classification problems. The first consists in detecting the sentiment (*negative* or *positive*) of a movie review, while the second is related to the classification of a comment based on different types of toxicity, such as *toxic*, *severe toxic*, *obscene*, *threat*, *insult* and *identity hate*.
This post is dedicated to the development of an artificial intelligence application capable of identifying the emotions expressed through the voice in spoken language. The classification model focuses on seven different emotions (*anger*, *boredom*, *disgust*, *fear*, *happiness*, *sadness*, *neutral*) and is enhanced with the attention mechanism.