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import time
import openai
#openai.api_key = "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"
# Sentence Generator (Decoder) for GPT-3 ...
def decoder_for_gpt3(input, max_length, temperature=0, engine="gpt-3.5-turbo-instruct"):
# GPT-3 API allows each users execute the API within 60 times in a minute ...
if engine == "gpt-3.5-turbo":
time.sleep(1)
response = openai.ChatCompletion.create(
model=engine,
messages=[
#{"role": "system", "content": "You need to answer commonsense questions."},
{"role": "user", "content": input}
],
max_tokens=max_length,
temperature=temperature,
stop=None
)
response = response["choices"][0]["message"]["content"]
else:
time.sleep(1)
response = openai.Completion.create(
model=engine,
prompt=input,
max_tokens=max_length,
stop=None,
temperature=temperature
)
response = response["choices"][0]["text"]
return response
def decoder_for_gpt3_consistency(input, max_length, temp=0.7, n=5, engine="gpt-3.5-turbo-instruct"):
# GPT-3 API allows each users execute the API within 60 times in a minute ...
if engine == "gpt-3.5-turbo":
time.sleep(1)
responses = openai.ChatCompletion.create(
model=engine,
messages=[
{"role": "user", "content": input}
],
max_tokens=max_length,
temperature=temp,
top_p=1,
n=5,
stop=["\n"],
)
responses = [responses["choices"][i]["message"]["content"] for i in range(n)]
else:
time.sleep(1)
responses = openai.Completion.create(
model=engine,
prompt=input,
max_tokens=max_length,
temperature=temp,
stop=["\n"],
n=5,
logprobs=5,
top_p=1,
)
responses = [responses["choices"][i]["text"] for i in range(n)]
return responses
def zero_shot(question):
input = question + " " + "Among A through E, the answer is"
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "You are a helpful assistant that answer commonsense questions."},
{"role": "user", "content": input}
]
)
response = response["choices"][0]["message"]["content"]
return response |