Hello,
When using a transformer model for text classification, one usually loads a model and then uses AutoModelForSequenceClassification to train the classifier over the N classes in the data.


My question: which model is actually used for classification? Is it a logistic model (with uses as input the CLS representation)?


In the case of several classes (say bad, neutral, good) the usual methodology in machine learning is to train several one-vs-all classifiers and then predict the label with most votes. Is this what is happening under the hood with huggingface?


Thanks!