Hey everyone! I’m using the transformers pipeline for sentiment classification to classify unlabeled text. Unfortunately, I’m getting some very awful results! For example, the sentence below is classified as negative with 0.99 percent certainty!
sent =  “The audience here in the hall has promised to remain silent.”
sentimentAnalysis = pipeline(task = “sentiment-analysis”)
print(sentimentAnalysis(sent))
# output : {‘label’: ‘NEGATIVE’, ‘score’: 0.9911394119262695}
Do you know what I can do to get better results for unlabeled text?
I actually tried training a large Roberta model on labeled text from kaggle and I’m getting so much better results, but I want to know why the pipeline is performing so bad, and what model it is actually using?