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benchmark.py
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import argparse
import datetime
import os
import json
import dataset_loader
from methods.utils import load_base_model, load_base_model_and_tokenizer
import numpy as np
from methods.supervised import run_supervised_experiment
from methods.detectgpt import run_detectgpt_experiments
from methods.gptzero import run_gptzero_experiment
from methods.radar import run_radar
from methods.sentinel import run_sentinel
from methods.metric_based import get_ll, get_rank, get_entropy, get_rank_GLTR, run_threshold_experiment, run_GLTR_experiment
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', type=str, default="TruthfulQA")
parser.add_argument('--detectLLM', type=str, default="ChatGPT")
parser.add_argument('--batch_size', type=int, default=16)
parser.add_argument('--base_model_name', type=str, default="gpt2-medium")
parser.add_argument('--mask_filling_model_name',
type=str, default="t5-large")
parser.add_argument('--cache_dir', type=str, default=".cache")
parser.add_argument('--DEVICE', type=str, default="cuda")
# params for DetectGPT
parser.add_argument('--pct_words_masked', type=float, default=0.3)
parser.add_argument('--span_length', type=int, default=2)
parser.add_argument('--n_perturbation_list', type=str, default="10")
parser.add_argument('--n_perturbation_rounds', type=int, default=1)
parser.add_argument('--chunk_size', type=int, default=20)
parser.add_argument('--n_similarity_samples', type=int, default=20)
parser.add_argument('--int8', action='store_true')
parser.add_argument('--half', action='store_true')
parser.add_argument('--do_top_k', action='store_true')
parser.add_argument('--top_k', type=int, default=40)
parser.add_argument('--do_top_p', action='store_true')
parser.add_argument('--top_p', type=float, default=0.96)
parser.add_argument('--buffer_size', type=int, default=1)
parser.add_argument('--mask_top_p', type=float, default=1.0)
parser.add_argument('--random_fills', action='store_true')
parser.add_argument('--random_fills_tokens', action='store_true')
# params for GPTZero
parser.add_argument('--gptzero_key', type=str, default="")
# params for Mixcase
parser.add_argument('--Mixcase_filename', type=str, default=None)
parser.add_argument('--MGT_only_GPT', action='store_true')
parser.add_argument('--test_only', action='store_true')
parser.add_argument('--train_threshold', type=int, default=10000)
parser.add_argument('--no_auc', action='store_true')
parser.add_argument('--only_supervised', action='store_true')
parser.add_argument('--train_with_mixcase', action='store_true')
parser.add_argument('--seed',type=int,default=0)
parser.add_argument('--ckpt_dir',type=str, default="./ckpt")
parser.add_argument('--log_name', type=str, default='Log')
parser.add_argument('--mixcase_threshold', type=float, default=0.8)
parser.add_argument('--transfer_filename', type=str, default=None)
parser.add_argument('--three_classes', action='store_true')
parser.add_argument('--finetune', action="store_true")
parser.add_argument('--mixcase_as_mgt', action="store_true")
args = parser.parse_args()
if args.dataset != "All":
data = dataset_loader.load(args.dataset, detectLLM=args.detectLLM)
else:
data = dataset_loader.load(args.dataset, filename1 = args.Mixcase_filename,
MGT_only_GPT = args.MGT_only_GPT,
test_only = args.test_only,
train_threshold = args.train_threshold,
no_auc = args.no_auc,
train_with_mixcase = args.train_with_mixcase,
seed = args.seed,
mixcase_threshold = args.mixcase_threshold,
filename2 = args.transfer_filename,
three_classes = args.three_classes,
mixcase_as_mgt = args.mixcase_as_mgt)
if not os.path.exists(args.ckpt_dir):
os.makedirs(args.ckpt_dir)
print(f"'{args.ckpt_dir}' are created.")
else:
print(f"'{args.ckpt_dir}' already exist.")
DEVICE = args.DEVICE
START_DATE = datetime.datetime.now().strftime('%Y-%m-%d')
START_TIME = datetime.datetime.now().strftime('%H-%M-%S-%f')
base_model_name = args.base_model_name.replace('/', '_')
SAVE_PATH = f"results/{base_model_name}-{args.mask_filling_model_name}/{args.dataset}"
if not os.path.exists(SAVE_PATH):
os.makedirs(SAVE_PATH)
print(f"Saving results to absolute path: {os.path.abspath(SAVE_PATH)}")
# write args to file
with open(os.path.join(SAVE_PATH, "args.json"), "w") as f:
json.dump(args.__dict__, f, indent=4)
mask_filling_model_name = args.mask_filling_model_name
batch_size = args.batch_size
n_perturbation_list = [int(x) for x in args.n_perturbation_list.split(",")]
n_perturbation_rounds = args.n_perturbation_rounds
n_similarity_samples = args.n_similarity_samples
cache_dir = args.cache_dir
if not os.path.exists(cache_dir):
os.makedirs(cache_dir)
print(f"Using cache dir {cache_dir}")
# get generative model
base_model, base_tokenizer = load_base_model_and_tokenizer(
args.base_model_name, cache_dir)
load_base_model(base_model, DEVICE)
def ll_criterion(text): return get_ll(
text, base_model, base_tokenizer, DEVICE)
def rank_criterion(text): return -get_rank(text,
base_model, base_tokenizer, DEVICE, log=False)
def logrank_criterion(text): return -get_rank(text,
base_model, base_tokenizer, DEVICE, log=True)
def entropy_criterion(text): return get_entropy(
text, base_model, base_tokenizer, DEVICE)
def GLTR_criterion(text): return get_rank_GLTR(
text, base_model, base_tokenizer, DEVICE)
outputs = []
if args.three_classes:
outputs.append(run_threshold_experiment(data, ll_criterion, "likelihood", test_only = args.test_only, no_auc=args.no_auc, ckpt_dir=args.ckpt_dir))
outputs.append(run_threshold_experiment(data, rank_criterion, "rank", test_only = args.test_only, no_auc=args.no_auc, ckpt_dir=args.ckpt_dir))
outputs.append(run_threshold_experiment(
data, logrank_criterion, "log_rank", test_only = args.test_only, no_auc=args.no_auc, ckpt_dir=args.ckpt_dir))
outputs.append(run_threshold_experiment(
data, entropy_criterion, "entropy", test_only = args.test_only, no_auc=args.no_auc, ckpt_dir=args.ckpt_dir))
outputs.append(run_GLTR_experiment(data, GLTR_criterion, "rank_GLTR", test_only = args.test_only, no_auc=args.no_auc, ckpt_dir=args.ckpt_dir))
outputs.append(run_supervised_experiment(data, model='distilbert-base-uncased',
cache_dir=cache_dir, batch_size=batch_size, DEVICE=DEVICE, pos_bit=1, num_labels=3, finetune=True, test_only = args.test_only, no_auc=args.no_auc, ckpt_dir=args.ckpt_dir))
outputs.append(run_supervised_experiment(data, model='Hello-SimpleAI/chatgpt-detector-roberta',
cache_dir=cache_dir, batch_size=batch_size, DEVICE=DEVICE, pos_bit=1, num_labels=3, test_only = args.test_only, no_auc=args.no_auc, ckpt_dir=args.ckpt_dir, finetune=True))
outputs.append(run_detectgpt_experiments(
args, data, base_model, base_tokenizer, test_only = args.test_only, no_auc=args.no_auc, ckpt_dir=args.ckpt_dir))
outputs.append(run_radar(data, DEVICE=DEVICE, finetune=args.finetune, no_auc=args.no_auc, ckpt_dir=args.ckpt_dir, test_only=args.test_only, three_classes=args.three_classes))
else:
if not args.only_supervised:
outputs.append(run_threshold_experiment(data, ll_criterion, "likelihood", test_only = args.test_only, no_auc=args.no_auc, ckpt_dir=args.ckpt_dir))
outputs.append(run_threshold_experiment(data, rank_criterion, "rank", test_only = args.test_only, no_auc=args.no_auc, ckpt_dir=args.ckpt_dir))
outputs.append(run_threshold_experiment(
data, logrank_criterion, "log_rank", test_only = args.test_only, no_auc=args.no_auc, ckpt_dir=args.ckpt_dir))
outputs.append(run_threshold_experiment(
data, entropy_criterion, "entropy", test_only = args.test_only, no_auc=args.no_auc, ckpt_dir=args.ckpt_dir))
outputs.append(run_GLTR_experiment(data, GLTR_criterion, "rank_GLTR", test_only = args.test_only, no_auc=args.no_auc, ckpt_dir=args.ckpt_dir))
# run GPTZero: pleaze specify your gptzero_key in the args
# outputs.append(run_gptzero_experiment(data, api_key=args.gptzero_key, test_only = args.test_only, no_auc=args.no_auc))
# run DetectGPT
outputs.append(run_detectgpt_experiments(
args, data, base_model, base_tokenizer, test_only = args.test_only, no_auc=args.no_auc, ckpt_dir=args.ckpt_dir))
outputs.append(run_sentinel(data, DEVICE=DEVICE, finetune=args.finetune, no_auc=args.no_auc, ckpt_dir=args.ckpt_dir, test_only=args.test_only))
outputs.append(run_radar(data, DEVICE=DEVICE, finetune=args.finetune, no_auc=args.no_auc, ckpt_dir=args.ckpt_dir, test_only=args.test_only))
outputs.append(run_supervised_experiment(data, model='roberta-base-openai-detector',
cache_dir=cache_dir, batch_size=batch_size, DEVICE=DEVICE, test_only = args.test_only, no_auc=args.no_auc, ckpt_dir=args.ckpt_dir, finetune=args.finetune))
outputs.append(run_supervised_experiment(data, model='Hello-SimpleAI/chatgpt-detector-roberta',
cache_dir=cache_dir, batch_size=batch_size, DEVICE=DEVICE, pos_bit=1, test_only = args.test_only, no_auc=args.no_auc, ckpt_dir=args.ckpt_dir, finetune=args.finetune))
outputs.append(run_supervised_experiment(data, model='distilbert-base-uncased',
cache_dir=cache_dir, batch_size=batch_size, DEVICE=DEVICE, pos_bit=1, finetune=True, test_only = args.test_only, no_auc=args.no_auc, ckpt_dir=args.ckpt_dir))
# save results
with open(f"logs/{args.log_name}", "a") as wf:
for row in outputs:
json.dump(row, wf)
wf.write("\n")
print("Finish")