import os import sys import json import time import openai import pickle import argparse import requests from tqdm import tqdm import torch from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaForCausalLM, LlamaTokenizer from fastchat.model import load_model, get_conversation_template, add_model_args from nltk.tag.mapping import _UNIVERSAL_TAGS import gradio as gr from transformers import pipeline uni_tags = list(_UNIVERSAL_TAGS) uni_tags[-1] = 'PUNC' bio_tags = ['B', 'I', 'O'] chunk_tags = ['ADJP', 'ADVP', 'CONJP', 'INTJ', 'LST', 'NP', 'O', 'PP', 'PRT', 'SBAR', 'UCP', 'VP'] syntags = ['NP', 'S', 'VP', 'ADJP', 'ADVP', 'SBAR', 'TOP', 'PP', 'POS', 'NAC', "''", 'SINV', 'PRN', 'QP', 'WHNP', 'RB', 'FRAG', 'WHADVP', 'NX', 'PRT', 'VBZ', 'VBP', 'MD', 'NN', 'WHPP', 'SQ', 'SBARQ', 'LST', 'INTJ', 'X', 'UCP', 'CONJP', 'NNP', 'CD', 'JJ', 'VBD', 'WHADJP', 'PRP', 'RRC', 'NNS', 'SYM', 'CC'] openai.api_key = "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" # determinant vs. determiner # https://wikidiff.com/determiner/determinant ents_prompt = ['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', ][7:] ents = ['NN', 'VB', 'JJ', 'RB', 'IN', 'CC', 'DT', 'NP', 'VP', 'ADJP', 'ADVP', 'PP', 'CONJP', 'CP', 'QP', 'CN', 'C', 'DC', 'FC', 'T', 'CT'][7:] ents_prompt_uni_tags = ['Verb', 'Noun', 'Pronoun', 'Adjective', 'Adverb', 'Preposition and Postposition', 'Coordinating Conjunction', 'Determiner', 'Cardinal Number', 'Particles or other function words', 'Words that cannot be assigned a POS tag', 'Punctuation'] ents = uni_tags + ents ents_prompt = ents_prompt_uni_tags + ents_prompt for i, j in zip(ents, ents_prompt): print(i, j) model_mapping = { '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', #'llama-7b': './llama/hf/7B', #'llama-13b': './llama/hf/13B', #'llama-30b': './llama/hf/30B', #'alpaca': './alpaca-7B', } with open('sample_uniform_1k_2.txt', 'r') as f: selected_idx = f.readlines() selected_idx = [int(i.strip()) for i in selected_idx]#[s:e] ptb = [] with open('ptb.jsonl', 'r') as f: for l in f: ptb.append(json.loads(l)) ## Prompt 1 template_all = '''Please output the in the following sentence without any additional text in json format: "{}"''' template_single = '''Please output any <{}> in the following sentence one per line without any additional text: "{}"''' ## Prompt 2 prompt2_pos = '''Please pos tag the following sentence using Universal POS tag set without generating any additional text: {}''' prompt2_chunk = '''Please do sentence chunking for the following sentence as in CoNLL 2000 shared task without generating any addtional text: {}''' prompt2_parse = '''Generate textual representation of the constituency parse tree of the following sentence using Penn TreeBank tag set without outputing any additional text: {}''' prompt2_chunk = '''Please chunk the following sentence in CoNLL 2000 format with BIO tags without outputing any additional text: {}''' ## Prompt 3 with open('demonstration_3_42_pos.txt', 'r') as f: demon_pos = f.read() with open('demonstration_3_42_chunk.txt', 'r') as f: demon_chunk = f.read() with open('demonstration_3_42_parse.txt', 'r') as f: demon_parse = f.read() # Your existing code theme = gr.themes.Soft() 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") #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") #alpaca_pipeline = pipeline(task="text2text-generation", model="./alpaca-7B") # Dropdown options for model and task model_options = list(model_mapping.keys()) task_options = ['POS', 'Chunking', 'Parsing'] # Function to process text based on model and task def process_text(model_name, task, text): gid_list = selected_idx[0:20] for gid in tqdm(gid_list, desc='Query'): text = ptb[gid]['text'] if model_name is 'gpt3.5': if task == 'POS': strategy1 = template_all.format(text) strategy2 = prompt2_pos.format(text) strategy3 = demon_pos return (gpt_pipeline(strategy1), gpt_pipeline(strategy2), gpt_pipeline(strategy3)) elif task == 'Chunking': strategy1 = template_all.format(text) strategy2 = prompt2_chunk.format(text) strategy3 = demon_chunk return (gpt_pipeline(strategy1), gpt_pipeline(strategy2), gpt_pipeline(strategy3)) elif task == 'Parsing': strategy1 = template_all.format(text) strategy2 = prompt2_parse.format(text) strategy3 = demon_parse return (gpt_pipeline(strategy1), gpt_pipeline(strategy2), gpt_pipeline(strategy3)) # Define prompts for each strategy based on the task #strategy_prompts = { # 'Strategy 1': template_all.format(text), # 'Strategy 2': { # 'POS': prompt2_pos.format(text), # 'Chunking': prompt2_chunk.format(text), # 'Parsing': prompt2_parse.format(text), # }.get(task, "Invalid Task Selection for Strategy 2"), # 'Strategy 3': { # 'POS': demon_pos, # 'Chunking': demon_chunk, # 'Parsing': demon_parse, # }.get(task, "Invalid Task Selection for Strategy 3"), #} # Gradio interface iface = gr.Interface( fn=process_text, inputs=[ gr.Dropdown(model_options, label="Select Model"), gr.Dropdown(task_options, label="Select Task"), gr.Textbox(label="Input Text", placeholder="Enter the text to process..."), ], outputs=[ gr.Textbox(label="Strategy 1 QA Result"), gr.Textbox(label="Strategy 2 Instruction Result"), gr.Textbox(label="Strategy 3 Structured Prompting Result"), ], title = "LLM Evaluator For Linguistic Scrutiny", theme = theme, live=False, ) iface.launch()