Fix/gpt3.5format

llm_judge/gen_gpt3.5_answer.py

@@ -7,6 +7,13 @@
 
 from typing import List
 
+from dotenv import load_dotenv
+
+load_dotenv()
+
+openai.api_key = os.getenv("OPENAI_API_KEY")
+openai.organization = os.getenv("OPENAI_ORGANIZATION")
+
 
 class GPT3_Demo(object):
     def __init__(
@@ -49,122 +56,39 @@ def run_gpt3(prompt_list):
     demo = GPT3_Demo(
         engine="text-davinci-003",  # text-davinci-003: best, text-ada-001: lowest price
         temperature=0,  # control randomness: lowring results in less random completion (0 ~ 1.0)
-        max_tokens=8,  # max number of tokens to generate (1 ~ 4,000)
+        max_tokens=2048,  # max number of tokens to generate (1 ~ 4,000)
         top_p=1,  # control diversity (0 ~ 1.0)
         frequency_penalty=0,  # how to penalize new tokens based on their existing frequency (0 ~ 2.0)
         presence_penalty=0,  # 这个是对于词是否已经出现过的惩罚，文档上说这个值调高可以增大谈论新topic的概率 (0 ~ 2.0)
-        best_of=3,  # 这个是说从多少个里选最好的，如果这里是10，就会生成10个然后选最好的，但是这样会更贵(1 ~ 20)
+        best_of=1,  # 这个是说从多少个里选最好的，如果这里是10，就会生成10个然后选最好的，但是这样会更贵(1 ~ 20)
         logprobs=1,
     )
     results = demo.get_multiple_sample(prompt_list)
     return results
 
 
-# use following codes to activate azure:
-
-
-# use openai api with group PRISM
-
-
-class Chat_Demo(object):
-    def __init__(
-        self,
-        model,
-        user_system,
-        temperature,
-        max_tokens,
-        top_p,
-        frequency_penalty,
-        presence_penalty,
-        n,
-        stream,
-        stop,
-        logit_bias,
-    ):
-        self.model = model
-        self.temperature = temperature
-        self.max_tokens = max_tokens
-        self.top_p = top_p
-        self.frequency_penalty = frequency_penalty
-        self.presence_penalty = presence_penalty
-        self.n = n
-        self.stream = stream
-        self.stop = stop
-        self.logit_bias = logit_bias
-        self.chat_list = []
-        self.chat_list = [{"role": "system", "content": user_system}]
-
-    def get_chat_output(self, user_prompt):
-        self.chat_list.append({"role": "user", "content": user_prompt})
-        response = openai.ChatCompletion.create(
-            model=self.model,
-            messages=self.chat_list,
-            temperature=self.temperature,
-            max_tokens=self.max_tokens,
-            top_p=self.top_p,
-            frequency_penalty=self.frequency_penalty,
-            presence_penalty=self.presence_penalty,
-            n=self.n,
-            stream=self.stream,
-            stop=self.stop,
-        )
-        reply = response["choices"][0]["message"]["content"]
-        self.chat_list.append(response["choices"][0]["message"])
-
-        return reply
-
-    def delete_last_chat(self):
-        self.chat_list.pop()
-        self.chat_list.pop()
-
-
-# usage template for chatgpt
-def run_chatgpt(user_prompt_list):
-    demo = Chat_Demo(
-        model="gpt-3.5-turbo-16k-0613",  # gpt-3.5-turbo: chatgpt with lowerest price, gpt-4: lateset version, higher price
-        # model="gpt-4",  # gpt-3.5-turbo: chatgpt with lowerest price, gpt-4: lateset version, higher price
-        user_system="You are a helpful assistant",  # add more description after this to fit your task, e.g., "you are a helpful assistant that translates English to Chinese." will be a good system for MT.
-        temperature=1.0,  # default 1.0, control randomness: lowring results in less random completion (0 ~ 2.0)
-        max_tokens=256,  # max number of tokens to generate (1 ~ 4,000)
-        top_p=1,  # default 1, control diversity (0 ~ 1.0), openai suggests not to alter this with temperature together
-        frequency_penalty=0,  # default 0, how to penalize new tokens based on their existing frequency (-2.0 ~ 2.0)
-        presence_penalty=0,  # default 0, 这个是对于词是否已经出现过的惩罚，文档上说这个值调高可以增大谈论新topic的概率 (-2.0 ~ 2.0)
-        stop="",  # manually control where to stop
-        stream=False,  # temporarily keep false
-        n=1,  # n choices of reply for each input
-        logit_bias=None,  # use the bias to control what tokens you want to appear or disappear, this function is NOT implemented in Chat_Demo now
-    )
-    reply = demo.get_chat_output(user_prompt_list)
-    return reply
-
-
 if __name__ == "__main__":
-    question = []
-    data_file = "./data/question_Japanese_ver.jsonl"
-    with open(data_file, "r") as f:
-        instruction_list = []
-        for line in tqdm(f.read().splitlines()):
-            tmp_dict = json.loads(line)
-            question.append(tmp_dict)
-            instruction_list.append(tmp_dict["text"][0:])
-        # examples = [l.strip() for l in instruction_list]
-        examples = instruction_list
+    data_file = "./data/jp_bench/question.jsonl"
+    with open(data_file) as f:
+        questions = [json.loads(line) for line in tqdm(f)]
+
     results = []
-    for index, example in tqdm(enumerate(examples)):
-        response = run_gpt3(example)
+    for question in tqdm(questions):
+        instruction = question["turns"][0]
+        response = run_gpt3(instruction)
         results.append(
             {
-                "question_id": question[index]["question_id"],
+                "question_id": question["question_id"],
                 "answer_id": shortuuid.uuid(),
                 "model_id": "gpt-3.5-davinci",
-                "choices": [{"index": 0, "turns": [response]}],
+                "choices": [{"index": 0, "turns": response}],
                 "tstamp": time.time(),
             }
         )
-    predictions_file = "./data/jp_bench/model_answer/gpt-3.5-davinci.jsonl"
+
+    predictions_file = "./data/jp_bench/model_answer/openai--gpt-3.5-davinci.json"
     dirname = os.path.dirname(predictions_file)
     os.makedirs(dirname, exist_ok=True)
-    with open(predictions_file, "w") as f:
+    with open(predictions_file, "w", encoding="utf-8") as f:
         for result in results:
-            json_line = json.dumps(result, ensure_ascii=False)
-            f.write(json_line + "\n")
+            f.write(json.dumps(result, ensure_ascii=False) + "\n")