ent, neut, and contra

app.py

@@ -9,16 +9,14 @@ tokenizer_bart = BartTokenizer.from_pretrained('facebook/bart-large-mnli')
 model_bart_sq = BartForSequenceClassification.from_pretrained('facebook/bart-large-mnli')
 
 
-# def predict(prompt):
-#     completion = model(prompt)[0]["generated_text"]
-#     return completion
-
 def zs(premise,hypothesis):
     input_ids = tokenizer_bart.encode(premise, hypothesis, return_tensors='pt')
     logits = model_bart_sq(input_ids)[0]
-    entail_contradiction_logits = logits[:,[0,2]]
+    entail_contradiction_logits = logits[:,[0,1,2]]
     probs = entail_contradiction_logits.softmax(dim=1)
-    true_prob = probs[:,1].item() * 100
-    return true_prob
+    contra_prob = round(probs[:,0].item() * 100,2)
+    neut_prob = round(probs[:,1].item() * 100,2)
+    entail_prob = round(probs[:,2].item() * 100,2)
+    return contra_prob, neut_prob, entail_prob
 
 gr.Interface(fn=zs, inputs=["text", "text"], outputs="text").launch()