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 developing 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.