Fine-tuning Bert/Roberta for multi-label sentiment analysis
Hi everyone, been really enjoying the content of HF so far and I’m excited to learn and join this fine community.I noticed that most, if not all, models deployed on the hub have either binary classification or 3 label classification (the 3rd one being “neutral”, in addition to “positive” and “negative”). And so, I had a few follow-up questions:Why do most models focus on binary classification? Is it that hard to extract more sentiments using the current models?I want to fine-tune either Bert/Roberta (would love to hear any pros/cons for each of these models) and add my own set of data (say 1000 sentences with 5 different label sentiments) - would I be able to fine-tune a model or am I doomed to start training the models from scratch?I read the quick tour for the transformer page and it is clearly explained how to fine-tune the Bert mode and add a different number of labels, but I didn’t understand whether I could just add labels and have the model try and guess between the new labels without me adding my own data & how and where I could plug in my own data and then fine-tune the last layer of the network and add, on my own, the final dense layer with the new desired labels.Looking forward to hearing your comments.Thank you!