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| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import time | |
| # Load the Vicuna 7B v1.3 LMSys model and tokenizer | |
| 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") | |
| with gr.Tab("POS"): | |
| gr.Markdown(" Description ") | |
| prompt_POS = gr.Textbox(show_label=False, placeholder="Write a prompt and press enter") | |
| 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-Based Prompting") | |
| with gr.Row(): | |
| vicuna_S2_chatbot_POS = gr.Chatbot(label="vicuna-7b") | |
| llama_S2_chatbot_POS = gr.Chatbot(label="llama-7b") | |
| gpt_S2_chatbot_POS = gr.Chatbot(label="gpt-3.5") | |
| clear = gr.ClearButton([prompt_POS, vicuna_S2_chatbot_POS]) | |
| gr.Markdown("Strategy 3 Structured Prompting") | |
| with gr.Row(): | |
| vicuna_S3_chatbot_POS = gr.Chatbot(label="vicuna-7b") | |
| llama_S3_chatbot_POS = gr.Chatbot(label="llama-7b") | |
| gpt_S3_chatbot_POS = gr.Chatbot(label="gpt-3.5") | |
| clear = gr.ClearButton([prompt_POS, vicuna_S3_chatbot_POS]) | |
| with gr.Tab("Chunk"): | |
| gr.Markdown(" Description ") | |
| prompt_CHUNK = gr.Textbox(show_label=False, placeholder="Write a prompt and press enter") | |
| gr.Markdown("Strategy 1 QA") | |
| with gr.Row(): | |
| vicuna_S1_chatbot_CHUNK = gr.Chatbot(label="vicuna-7b") | |
| llama_S1_chatbot_CHUNK = gr.Chatbot(label="llama-7b") | |
| gpt_S1_chatbot_CHUNK = gr.Chatbot(label="gpt-3.5") | |
| clear = gr.ClearButton([prompt_CHUNK, vicuna_S1_chatbot_CHUNK]) | |
| gr.Markdown("Strategy 2 Instruction") | |
| with gr.Row(): | |
| vicuna_S2_chatbot_CHUNK = gr.Chatbot(label="vicuna-7b") | |
| llama_S2_chatbot_CHUNK = gr.Chatbot(label="llama-7b") | |
| gpt_S2_chatbot_CHUNK = gr.Chatbot(label="gpt-3.5") | |
| clear = gr.ClearButton([prompt_CHUNK, vicuna_S2_chatbot_CHUNK]) | |
| gr.Markdown("Strategy 3 Structured Prompting") | |
| with gr.Row(): | |
| vicuna_S3_chatbot_CHUNK = gr.Chatbot(label="vicuna-7b") | |
| llama_S3_chatbot_CHUNK = gr.Chatbot(label="llama-7b") | |
| gpt_S3_chatbot_CHUNK = gr.Chatbot(label="gpt-3.5") | |
| clear = gr.ClearButton([prompt_CHUNK, vicuna_S3_chatbot_CHUNK]) | |
| def respond(message, chat_history): | |
| input_ids = tokenizer.encode(message, return_tensors="pt") | |
| output_ids = model.generate(input_ids, max_length=50, num_beams=5, no_repeat_ngram_size=2) | |
| bot_message = tokenizer.decode(output_ids[0], skip_special_tokens=True) | |
| chat_history.append((message, bot_message)) | |
| time.sleep(2) | |
| return "", chat_history | |
| 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, [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() | |