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api.py
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api.py
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import argparse
from utils import *
def cot(method, question):
args = parse_arguments()
decoder = Decoder()
args.method = method
if args.method != "zero_shot_cot":
if args.method == "auto_cot":
args.demo_path = "demos/multiarith_auto"
else:
args.demo_path = "demos/multiarith_manual"
demo = create_demo_text(args, cot_flag=True)
else:
demo = None
x = "Q: " + question + "\n" + "A:"
print('*****************************')
print("Test Question:")
print(question)
print('*****************************')
if args.method == "zero_shot":
x = x + " " + args.direct_answer_trigger_for_zeroshot
elif args.method == "zero_shot_cot":
x = x + " " + args.cot_trigger
elif args.method == "manual_cot":
x = demo + x
elif args.method == "auto_cot":
x = demo + x + " " + args.cot_trigger
else:
raise ValueError("method is not properly defined ...")
print("Prompted Input:")
print(x.replace("\n\n", "\n").strip())
print('*****************************')
max_length = args.max_length_cot if "cot" in args.method else args.max_length_direct
z = decoder.decode(args, x, max_length)
z = z.replace("\n\n", "\n").replace("\n", "").strip()
if args.method == "zero_shot_cot":
z2 = x + z + " " + args.direct_answer_trigger_for_zeroshot_cot
max_length = args.max_length_direct
pred = decoder.decode(args, z2, max_length)
print("Output:")
print(z + " " + args.direct_answer_trigger_for_zeroshot_cot + " " + pred)
print('*****************************')
else:
pred = z
print("Output:")
print(pred)
print('*****************************')
def parse_arguments():
parser = argparse.ArgumentParser(description="Zero-shot-CoT")
parser.add_argument("--max_num_worker", type=int, default=0, help="maximum number of workers for dataloader")
parser.add_argument(
"--model", type=str, default="gpt3-xl", help="model used for decoding. Note that 'gpt3' are the smallest models."
)
parser.add_argument(
"--method", type=str, default="auto_cot", choices=["zero_shot", "zero_shot_cot", "few_shot", "few_shot_cot", "auto_cot"], help="method"
)
parser.add_argument(
"--cot_trigger_no", type=int, default=1, help="A trigger sentence that elicits a model to execute chain of thought"
)
parser.add_argument(
"--max_length_cot", type=int, default=256, help="maximum length of output tokens by model for reasoning extraction"
)
parser.add_argument(
"--max_length_direct", type=int, default=32, help="maximum length of output tokens by model for answer extraction"
)
parser.add_argument(
"--limit_dataset_size", type=int, default=0, help="whether to limit test dataset size. if 0, the dataset size is unlimited and we use all the samples in the dataset for testing."
)
parser.add_argument(
"--api_time_interval", type=float, default=1.0, help=""
)
parser.add_argument(
"--temperature", type=float, default=0, help=""
)
parser.add_argument(
"--log_dir", type=str, default="./log/", help="log directory"
)
args = parser.parse_args()
args.direct_answer_trigger_for_fewshot = "The answer is"
args.direct_answer_trigger_for_zeroshot = "The answer is"
args.direct_answer_trigger_for_zeroshot_cot = "The answer is"
args.cot_trigger = "Let's think step by step."
return args