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run_1_prompt.py
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run_1_prompt.py
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from dataset import JSONLDataset, TabularDataset, PickleDataset
import models.openai as openai
#from util import parse_example, parse_tsv_example, parse_qaner_example, score_sets
import numpy as np
import time
import re
from dotenv import load_dotenv
import argparse
import random
from tqdm.auto import tqdm
import os, pdb
import json
import shutil
import logging
from datetime import datetime
import signal
import time
def run_prompt(prompt, model):
completion = model.complete(prompt)
time.sleep(10)
if completion is None or completion == '':
return "Could not complete prompt"
return completion
def construct_prompt(srcfile, promptfile, egs):
ds = JSONLDataset(srcfile)
egs = []
for idx in eg_idxs:
egs.append(ds[idx])
for eg in egs:
for (ptok, gtok) in zip(eg['pred_labels'], eg['gold_labels']):
if ptok == gtok:
print(ptok, end=' ')
else:
print(f'[{ptok}]', end=' ')
print()
def get_prompt_eg_acc(srcfile, eg_idxs):
ds = JSONLDataset(srcfile)
egs = []
for idx in eg_idxs:
egs.append(ds[idx])
for eg in egs:
for (ptok, gtok) in zip(eg['pred_labels'], eg['gold_labels']):
if ptok == gtok:
print(ptok, end=' ')
else:
print(f'[{ptok}->{gtok}]', end=' ')
print()
def create_model():
load_dotenv(os.path.join(os.path.dirname(__file__), '../.env'))
openai.setup_api_key(os.environ.get('OPENAI_API_KEY'))
model_args = openai.ChatGPT.DEFAULT_ARGS
model_args['engine'] = "gpt-4-turbo"
model_args['request_timeout'] = 200
# change model to llama to use llama
model = openai.ChatGPT(model_args)
model.default_args['temperature'] = 0
return openai.ChatGPT(model_args)
def parse_response(response):
lines = response.strip().split('\n')
pred = [a.split('\t') for a in lines if '\t' in a]
return list(zip(*pred))
model = create_model()
def print_results(prompt, model):
print(run_prompt(prompt, model))
inp_prompt = open("single_prompt.txt").read()
print_results(inp_prompt, model)