test

app.py

@@ -7,6 +7,18 @@ model_name = "lmsys/vicuna-7b-v1.3"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name)
 
+## Task 1
+# msg = template_all.format(text)
+template_all = '''Output the <Noun, Verb, Adjective, Adverb, Preposition/Subord, Coordinating Conjunction, Cardinal Number, Determiner, Noun Phrase, Verb Phrase, Adjective Phrase, Adverb Phrase, Preposition Phrase, Conjunction Phrase, Coordinate Phrase, Quantitave Phrase, Complex Nominal, Clause, Dependent Clause, Fragment Clause, T-unit, Complex T-unit, Fragment T-unit> in the following sentence without additional text in json format: "{}"'''
+# msg = template_single.format(ents_prompt[eid], text)
+template_single = '''Output any <{}> in the following sentence one per line without additional text: "{}"'''
+
+## Task 2
+prompt2_pos = '''POS tag the following sentence using Universal POS tag set without generating additional text: {}'''
+prompt2_chunk = '''Do sentence chunking for the following sentence as in CoNLL 2000 shared task without generating addtional text: {}'''
+
+## Task 3
+
 with gr.Blocks() as demo:
     gr.Markdown("# LLM Evaluator With Linguistic Scrutiny")
 
@@ -15,13 +27,13 @@ with gr.Blocks() as demo:
 
         prompt_POS = gr.Textbox(show_label=False, placeholder="Write a prompt and press enter")
 
-        gr.Markdown("Strategy 1 QA")
+        gr.Markdown("Strategy 1 QA-Based Prompting")
         with gr.Row():
             vicuna_S1_chatbot_POS = gr.Chatbot(label="vicuna-7b")
             llama_S1_chatbot_POS = gr.Chatbot(label="llama-7b")
             gpt_S1_chatbot_POS = gr.Chatbot(label="gpt-3.5")
         clear = gr.ClearButton([prompt_POS, vicuna_S1_chatbot_POS])
-        gr.Markdown("Strategy 2 Instruction")
+        gr.Markdown("Strategy 2 Instruction-Based Prompting")
         with gr.Row():
             vicuna_S2_chatbot_POS = gr.Chatbot(label="vicuna-7b")
             llama_S2_chatbot_POS = gr.Chatbot(label="llama-7b")
@@ -67,12 +79,12 @@ with gr.Blocks() as demo:
         time.sleep(2)
         return "", chat_history
 
-    prompt_POS.submit(respond, [prompt_POS, vicuna_S1_chatbot_POS], [prompt_POS, vicuna_S1_chatbot_POS])
-    prompt_POS.submit(respond, [prompt_POS, vicuna_S2_chatbot_POS], [prompt_POS, vicuna_S2_chatbot_POS])
+    prompt_POS.submit(respond, [template_all.format(prompt_POS), vicuna_S1_chatbot_POS], [template_all.format(prompt_POS), vicuna_S1_chatbot_POS])
+    prompt_POS.submit(respond, [prompt2_pos.format(prompt_POS), vicuna_S2_chatbot_POS], [prompt2_pos.format(prompt_POS), vicuna_S2_chatbot_POS])
     prompt_POS.submit(respond, [prompt_POS, vicuna_S3_chatbot_POS], [prompt_POS, vicuna_S3_chatbot_POS])
 
-    prompt_CHUNK.submit(respond, [prompt_CHUNK, vicuna_S1_chatbot_CHUNK], [prompt_CHUNK, vicuna_S1_chatbot_CHUNK])
-    prompt_CHUNK.submit(respond, [prompt_CHUNK, vicuna_S2_chatbot_CHUNK], [prompt_CHUNK, vicuna_S2_chatbot_CHUNK])
+    prompt_CHUNK.submit(respond, [template_all.format(prompt_CHUNK), vicuna_S1_chatbot_CHUNK], [template_all.format(prompt_CHUNK), vicuna_S1_chatbot_CHUNK])
+    prompt_CHUNK.submit(respond, [prompt2_chunk.format(prompt_CHUNK), vicuna_S2_chatbot_CHUNK], [prompt2_chunk.format(prompt_CHUNK), vicuna_S2_chatbot_CHUNK])
     prompt_CHUNK.submit(respond, [prompt_CHUNK, vicuna_S3_chatbot_CHUNK], [prompt_CHUNK, vicuna_S3_chatbot_CHUNK])
 
 demo.launch()