Thanks a lot for replying.

Are you framing your classification problem as a sequence generation task?

I dont know what this means. What I’ve been trying is: given a news headline, predict the sentiment of it as being positive or negative, just like how the financial phrasebank dataset looks like.

what types of labels do you have for your training data?

I’m trying to use the bart-large-mnli model and finetune it on the financial phrasebank dataset. The financial phrasebank data has 3 labels: positive, neutral and negative.

Are the labels text/sequence or a finite number of categories?

Yes. Positive, negative and neutral.

If your task is classification I believe you’re using the wrong model class. You could probably use BertForSequenceClassification for a sentiment analysis task as has been done in the link below:

Thanks for the link. I dont know why but if I use TrainingArguments and Trainer, I either get an error as “CUDA out of memory” or “Expect input batch size to meet targeted batch size”.
Thanks again for your inputs.