description and probabilities

app.py

@@ -8,6 +8,10 @@ from transformers import BartForSequenceClassification, BartTokenizer
 tokenizer_bart = BartTokenizer.from_pretrained('facebook/bart-large-mnli')
 model_bart_sq = BartForSequenceClassification.from_pretrained('facebook/bart-large-mnli')
 
+title="Stance Detection using Zero Shot"
+
+description="Welcome to the side where the grass is greener. This is a simple tool which was created with an aim to stance towards a given entity in a sentence. However, this is not the only use case of it!"
+
 
 def zs(premise,hypothesis):
     input_ids = tokenizer_bart.encode(premise, hypothesis, return_tensors='pt')
@@ -15,9 +19,9 @@ def zs(premise,hypothesis):
     # entail_contradiction_logits = logits[:,[0,1,2]]
     entail_contradiction_logits = logits[:,[0,2]]
     probs = entail_contradiction_logits.softmax(dim=1)
-    contra_prob = round(probs[:,0].item() * 100,2)
-    # neut_prob = round(probs[:,1].item() * 100,2)
-    entail_prob = round(probs[:,1].item() * 100,2)
+    contra_prob = round(probs[:,0].item(),4)
+    # neut_prob = round(probs[:,1].item(),4)
+    entail_prob = round(probs[:,1].item(),4)
     # return contra_prob, neut_prob, entail_prob
     return contra_prob, entail_prob