Hello,
I got a really basic question on the whole BERT/finetune BERT for classification topic:
I got a dataset with customer reviews which consists of 7 different labels such as “Customer Service”, “Tariff”, “Provider related” etc. .
My dataset contains 12700 not labelled customer reviews and I labelled 1100 reviews for my classification task. Now to my questions:


Could it be enough to take an existing BERT model and fine-tune it with AutoModelForSequenceClassification on my specific task?


Are the 1100 labelled reviews (around 150 per each class) enough to train that?


What are other approaches?


I am completely new to the whole NLP task and working on it for 3 weeks. I just understood that more traditional approaches are often outperformed by transformers…
Thanks in advance!