updated with vicuna

app.py

@@ -50,8 +50,8 @@ for i, j in zip(ents, ents_prompt):
     print(i, j)
 
 model_mapping = {
-    'gpt3.5': 'gpt2',
-    #'vicuna-7b': 'lmsys/vicuna-7b-v1.3',
+    #'gpt3.5': 'gpt2',
+    'vicuna-7b': 'lmsys/vicuna-7b-v1.3',
     #'vicuna-13b': 'lmsys/vicuna-13b-v1.3',
     #'vicuna-33b': 'lmsys/vicuna-33b-v1.3',
     #'fastchat-t5': 'lmsys/fastchat-t5-3b-v1.0',
@@ -94,8 +94,8 @@ with open('demonstration_3_42_parse.txt', 'r') as f:
 theme = gr.themes.Soft()
 
 
-gpt_pipeline = pipeline(task="text2text-generation", model="gpt2")
-#vicuna7b_pipeline = pipeline(task="text2text-generation", model="lmsys/vicuna-7b-v1.3")
+#gpt_pipeline = pipeline(task="text2text-generation", model="gpt2")
+vicuna7b_pipeline = pipeline(task="text2text-generation", model="lmsys/vicuna-7b-v1.3")
 #vicuna13b_pipeline = pipeline(task="text2text-generation", model="lmsys/vicuna-13b-v1.3")
 #vicuna33b_pipeline = pipeline(task="text2text-generation", model="lmsys/vicuna-33b-v1.3")
 #fastchatT5_pipeline = pipeline(task="text2text-generation", model="lmsys/fastchat-t5-3b-v1.0")
@@ -116,53 +116,34 @@ def process_text(model_name, task, text):
     for gid in tqdm(gid_list, desc='Query'):
             text = ptb[gid]['text']
     
-    if model_name == 'gpt3.5': 
+    if model_name == 'vicuna-7b': 
         if task == 'POS':
-            from transformers import pipeline
-            
-            # Load the pipeline for part-of-speech tagging using the vicuna7b model
-            pos_pipeline = pipeline("ner", model="finiteautomata/bert2crf4ner-pos", tokenizer="finiteautomata/bert2crf4ner-pos")
-            
-            # Define the input phrase
-            input_phrase = "A cat loves the beautiful dog in the neighbor's house"
-            
-            # Use the pipeline to get the part-of-speech tags
-            pos_tags = pos_pipeline(input_phrase)
-            
-            # Print the output
-            print(pos_tags)
-
             strategy1_format = template_all.format(text)
             strategy2_format = prompt2_pos.format(text)
             strategy3_format = demon_pos
 
-            result1 = gpt_pipeline(strategy1_format)
-            result2 = gpt_pipeline(strategy2_format)
-            result3 = gpt_pipeline(strategy3_format)
-
-            generated_text1 = result1[0]['generated_text']
-            generated_text2 = result2[0]['generated_text']
-            generated_text3 = result3[0]['generated_text']
-
-            return (generated_text1, generated_text2, generated_text3)
-        # elif task == 'Chunking':
-        #     strategy1_format = template_all.format(text)
-        #     strategy2_format = prompt2_chunk.format(text)
-        #     strategy3_format = demon_chunk
-
-        #     result1 = gpt_pipeline(strategy1_format)[0]['generated_text']
-        #     result2 = gpt_pipeline(strategy2_format)[0]['generated_text']
-        #     result3 = gpt_pipeline(strategy3_format)[0]['generated_text']
-        #     return (result1, result2, result3)
-        # elif task == 'Parsing':
-        #     strategy1_format = template_all.format(text)
-        #     strategy2_format = prompt2_parse.format(text)
-        #     strategy3_format = demon_parse
+            result1 = vicuna7b_pipeline(strategy1_format)[0]['generated_text']
+            result2 = vicuna7b_pipeline(strategy2_format)[0]['generated_text']
+            result3 = vicuna7b_pipeline(strategy3_format)[0]['generated_text']
+            return (result1, result2, result3)
+        elif task == 'Chunking':
+            strategy1_format = template_all.format(text)
+            strategy2_format = prompt2_chunk.format(text)
+            strategy3_format = demon_chunk
+
+            result1 = vicuna7b_pipeline(strategy1_format)[0]['generated_text']
+            result2 = vicuna7b_pipeline(strategy2_format)[0]['generated_text']
+            result3 = vicuna7b_pipeline(strategy3_format)[0]['generated_text']
+            return (result1, result2, result3)
+        elif task == 'Parsing':
+            strategy1_format = template_all.format(text)
+            strategy2_format = prompt2_parse.format(text)
+            strategy3_format = demon_parse
             
-        #     result1 = gpt_pipeline(strategy1_format)[0]['generated_text']
-        #     result2 = gpt_pipeline(strategy2_format)[0]['generated_text']
-        #     result3 = gpt_pipeline(strategy3_format)[0]['generated_text']
-        #     return (result1, result2, result3)
+            result1 = vicuna7b_pipeline(strategy1_format)[0]['generated_text']
+            result2 = vicuna7b_pipeline(strategy2_format)[0]['generated_text']
+            result3 = vicuna7b_pipeline(strategy3_format)[0]['generated_text']
+            return (result1, result2, result3)
 
 # Gradio interface
 iface = gr.Interface(