added anther model to test

app.py

@@ -54,7 +54,7 @@ model_mapping = {
     #'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',
+    'fastchat-t5': 'lmsys/fastchat-t5-3b-v1.0',
     #'llama-7b': './llama/hf/7B',
     #'llama-13b': './llama/hf/13B',
     #'llama-30b': './llama/hf/30B',
@@ -98,7 +98,7 @@ gpt_pipeline = pipeline(task="text-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")
+fastchatT5_pipeline = pipeline(task="text2text-generation", model="lmsys/fastchat-t5-3b-v1.0")
 #llama7b_pipeline = pipeline(task="text2text-generation", model="./llama/hf/7B")
 #llama13b_pipeline = pipeline(task="text2text-generation", model="./llama/hf/13B")
 #llama30b_pipeline = pipeline(task="text2text-generation", model="./llama/hf/30B")
@@ -145,6 +145,34 @@ def process_text(model_name, task, text):
             response2 = gpt_pipeline(strategy2)[0]['generated_text']
             response3 = gpt_pipeline(strategy3)[0]['generated_text']
             return (response1, response2, response3)
+    elif model_name == 'fastchat-t5': 
+        if task == 'POS':
+            strategy1 = template_all.format(text)
+            strategy2 = prompt2_pos.format(text)
+            strategy3 = demon_pos
+
+            response1 = fastchatT5_pipeline(strategy1)[0]['generated_text']
+            response2 = fastchatT5_pipeline(strategy2)[0]['generated_text']
+            response3 = fastchatT5_pipeline(strategy3)[0]['generated_text']
+            return (response1, response2, response3)
+        elif task == 'Chunking':
+            strategy1 = template_all.format(text)
+            strategy2 = prompt2_chunk.format(text)
+            strategy3 = demon_chunk
+
+            response1 = fastchatT5_pipeline(strategy1)[0]['generated_text']
+            response2 = fastchatT5_pipeline(strategy2)[0]['generated_text']
+            response3 = fastchatT5_pipeline(strategy3)[0]['generated_text']
+            return (response1, response2, response3)
+        elif task == 'Parsing':
+            strategy1 = template_all.format(text)
+            strategy2 = prompt2_parse.format(text)
+            strategy3 = demon_parse
+            
+            response1 = fastchatT5_pipeline(strategy1)[0]['generated_text']
+            response2 = fastchatT5_pipeline(strategy2)[0]['generated_text']
+            response3 = fastchatT5_pipeline(strategy3)[0]['generated_text']
+            return (response1, response2, response3)
     
     # Define prompts for each strategy based on the task
     #strategy_prompts = {