Auto Vs DistilBert for Classification : Accuracy/F1 varies a lot
I am trying to fine-tune the “distilbert-base-uncased” with the “emotions” dataset (6 labels).I am using DistilBertTokenizer.When I useDistilBertForSequenceClassification.from_pretrained()I get an accuracy/f1 of only 35%,but when II useAutoModelForSequenceClassificationfrom_pretrained()I get an accuracy/f1 over 90%I thought it’s better to use a specific model than the Auto ones but it seems Auto works much better, what could be a logical explanation for this?Thanks.