Switched qa model

app.py

@@ -1,25 +1,30 @@
 import gradio as gr
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 from transformers import T5Tokenizer, T5ForConditionalGeneration
+from transformers import pipeline
 import torch
 
 device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
+
 te_tokenizer = AutoTokenizer.from_pretrained('MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli')
 te_model = AutoModelForSequenceClassification.from_pretrained('MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli').to(device)
-qa_tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-large")
-qa_model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large", device_map="auto")
+qa_pipeline = pipeline("question-answering", model='distilbert/distilbert-base-cased-distilled-squad')
+qa_tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base")
+qa_model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base", device_map="auto")
 
 def predict(context, intent, multi_class):
+    print(context, intent)
     input_text = "What is the opposite of " + intent + "?"
     input_ids = qa_tokenizer(input_text, return_tensors="pt").input_ids.to(device)
     opposite_output = qa_tokenizer.decode(qa_model.generate(input_ids, max_length=2)[0], skip_special_tokens=True)
-    input_text = "What object is the following describing: " + context
-    input_ids = qa_tokenizer(input_text, return_tensors="pt").input_ids.to(device)
-    object_output = qa_tokenizer.decode(qa_model.generate(input_ids, max_length=2)[0], skip_special_tokens=True)
+    input_text = "What object/thing is being described in the entire sentence?"
+    object_output = qa_pipeline(question=input_text, context=context, max_answer_len=2)['answer']
     batch = ['The ' + object_output + ' is ' + intent, 'The ' + object_output + ' is ' + opposite_output, 'The ' + object_output + ' is neither ' + intent + ' nor ' + opposite_output]
     outputs = []
     normal = 0
     
+    print(batch)
+    
     for i, hypothesis in enumerate(batch):
         input_ids = te_tokenizer.encode(context, hypothesis, return_tensors='pt').to(device)
         
@@ -33,6 +38,7 @@ def predict(context, intent, multi_class):
         else:
             probs = torch.exp(logits)
         outputs.append(probs)
+
     # calculate the stochastic vector for it being neither the positive or negative class
     perfect_prob = outputs[2]
     # -> [entailment, contradiction] for perfect
@@ -40,15 +46,18 @@ def predict(context, intent, multi_class):
     # -> [entailment, neutral, contradiction] for positive
     outputs[1] = outputs[1].flip(dims=[0])
     
+    print(outputs)
+    print(perfect_prob)
+    
     # combine the negative and positive class by summing by the opposite of the negative class
-    aggregated = (outputs[0] + outputs[1])/2
+    aggregated = (outputs[0]+outputs[1])/2
+    
+    print(aggregated)
     
     # multiplying vectors
-    aggregated[1] = aggregated[1] * perfect_prob[0]
+    aggregated[1] = aggregated[1] + perfect_prob[0]
     aggregated[0] = aggregated[0] * perfect_prob[1]
     aggregated[2] = aggregated[2] * perfect_prob[1]
-    
-    aggregated = torch.sqrt(aggregated)
 
     # multiple true classes
     if (multi_class):
@@ -58,9 +67,8 @@ def predict(context, intent, multi_class):
     else:
         aggregated = aggregated.softmax(dim=0)
         normal = normal.softmax(dim=0)
-    aggregated = aggregated.tolist()
     return {"agree": aggregated[0], "neutral": aggregated[1], "disagree": aggregated[2]}, {"agree": normal[0], "neutral": normal[1], "disagree": normal[2]}
-examples = [["These are my absolute favorite cargos in my closet. I’m 5’7 and they’re actually long enough for me. I’m 165lbs and ordered an M & it fits nice and loose just how I wanted it. The adjustable waist band is awesome!", "long"], ["I feel strongly about politics in the US", "long"], ["The pants are long", "long"], ["The pants are slightly long", "long"]]
+examples = [["These are so warm and comfortable. I’m 5’7”, 140 lbs, size 6-8 and Medium is a great fit. They wash and dry nicely too. The jogger style is the only style I can wear in this brand - the others are way too long so I had to return.", "long"], ["I feel strongly about politics in the US", "long"], ["The pants are long", "long"], ["The pants are slightly long", "long"]]
 
 gradio_app = gr.Interface(
     predict,

gradio_cached_examples/18/log.csv

@@ -1,5 +1,5 @@
 With Postprocessing,Without Postprocessing,flag,username,timestamp
-"{""label"":""neutral"",""confidences"":[{""label"":""neutral"",""confidence"":0.3989219665527344},{""label"":""agree"",""confidence"":0.3555052578449249},{""label"":""disagree"",""confidence"":0.24557280540466309}]}","{""label"":""agree"",""confidences"":[{""label"":""agree"",""confidence"":0.9896149635314941},{""label"":""neutral"",""confidence"":0.008607939817011356},{""label"":""disagree"",""confidence"":0.0017770910635590553}]}",,,2024-03-14 17:33:32.082554
-"{""label"":""neutral"",""confidences"":[{""label"":""neutral"",""confidence"":0.9888092875480652},{""label"":""agree"",""confidence"":0.0059028444811701775},{""label"":""disagree"",""confidence"":0.005287905689328909}]}","{""label"":""neutral"",""confidences"":[{""label"":""neutral"",""confidence"":0.9971976280212402},{""label"":""agree"",""confidence"":0.0019434979185461998},{""label"":""disagree"",""confidence"":0.0008588095079176128}]}",,,2024-03-14 17:33:43.894521
-"{""label"":""agree"",""confidences"":[{""label"":""agree"",""confidence"":0.999862790107727},{""label"":""disagree"",""confidence"":0.00007605748396599665},{""label"":""neutral"",""confidence"":0.00006114941061241552}]}","{""label"":""agree"",""confidences"":[{""label"":""agree"",""confidence"":0.9909017086029053},{""label"":""neutral"",""confidence"":0.008000608533620834},{""label"":""disagree"",""confidence"":0.0010977860074490309}]}",,,2024-03-14 17:33:56.295829
-"{""label"":""agree"",""confidences"":[{""label"":""agree"",""confidence"":0.5945301651954651},{""label"":""neutral"",""confidence"":0.26899591088294983},{""label"":""disagree"",""confidence"":0.1364738941192627}]}","{""label"":""agree"",""confidences"":[{""label"":""agree"",""confidence"":0.8074565529823303},{""label"":""neutral"",""confidence"":0.1722831279039383},{""label"":""disagree"",""confidence"":0.02026035077869892}]}",,,2024-03-14 17:34:11.315778
+"{""label"":""neutral"",""confidences"":[{""label"":""neutral"",""confidence"":0.7777004837989807},{""label"":""disagree"",""confidence"":0.11701762676239014},{""label"":""agree"",""confidence"":0.10528186708688736}]}","{""label"":""disagree"",""confidences"":[{""label"":""disagree"",""confidence"":0.9934408068656921},{""label"":""neutral"",""confidence"":0.006133753340691328},{""label"":""agree"",""confidence"":0.0004254970990587026}]}",,,2024-03-18 01:16:39.407608
+"{""label"":""neutral"",""confidences"":[{""label"":""neutral"",""confidence"":1.0},{""label"":""agree"",""confidence"":5.289930995508298e-31},{""label"":""disagree"",""confidence"":4.9565565658354345e-31}]}","{""label"":""neutral"",""confidences"":[{""label"":""neutral"",""confidence"":0.9979484677314758},{""label"":""agree"",""confidence"":0.0014055748470127583},{""label"":""disagree"",""confidence"":0.0006459908909164369}]}",,,2024-03-18 01:16:40.307454
+"{""label"":""agree"",""confidences"":[{""label"":""agree"",""confidence"":1.0},{""label"":""neutral"",""confidence"":0.0},{""label"":""disagree"",""confidence"":0.0}]}","{""label"":""agree"",""confidences"":[{""label"":""agree"",""confidence"":0.9909017086029053},{""label"":""neutral"",""confidence"":0.008000608533620834},{""label"":""disagree"",""confidence"":0.0010977860074490309}]}",,,2024-03-18 01:16:41.177826
+"{""label"":""agree"",""confidences"":[{""label"":""agree"",""confidence"":0.673342764377594},{""label"":""neutral"",""confidence"":0.2709066867828369},{""label"":""disagree"",""confidence"":0.0557505264878273}]}","{""label"":""agree"",""confidences"":[{""label"":""agree"",""confidence"":0.8074565529823303},{""label"":""neutral"",""confidence"":0.1722831279039383},{""label"":""disagree"",""confidence"":0.02026035077869892}]}",,,2024-03-18 01:16:41.822843