优化评分部分文件结构，增加对比式gpt评分

.gitignore

@@ -167,3 +167,5 @@ cython_debug/
 /ptuning/
 /logs/
 /data/
+/qlora/
+/metrics/

compare.py

@@ -0,0 +1,35 @@
+import pandas as pd
+
+# 读取两个csv文件
+human_score_df = pd.read_csv('logs/other/human.csv')
+machine_score_df = pd.read_csv('logs/other/result_diff_test_score_53.84256314043283.csv')
+
+result_df = pd.DataFrame(columns=['question', 'answer', 'predict_finetune', 'predict_origin', 'acc_finetune', 'human_acc_finetune', 'acc_origin', 'human_acc_origin', 'fluency_finetune', 'human_fluency_finetune', 'diff_score', 'human_diff_score'])
+result_df_row_index = 0
+for row_index, row in machine_score_df.iterrows():
+    acc_finetune_diff = row['acc_finetune'] - human_score_df.loc[row_index, '准确度（微调后']
+    acc_origin_diff = row['acc_origin'] - human_score_df.loc[row_index, '准确度（微调前']
+    fluency_finetune_diff = row['fluency_finetune'] - human_score_df.loc[row_index, '流畅度（微调后']
+    diff_score_diff = row['diff_score'] - human_score_df.loc[row_index, '是否超过原模型']
+    print("准确度（微调后）差值：", abs(acc_finetune_diff),end=' ')
+    print("准确度（微调前）差值：", abs(acc_origin_diff),end=' ')
+    print("流畅度（微调后）差值：", abs(fluency_finetune_diff),end=' ')
+    print("是否超过原模型差值：", abs(diff_score_diff))
+    if abs(acc_finetune_diff) >= 2:
+        result_df.loc[result_df_row_index, 'question'] = machine_score_df.loc[row_index, 'question']
+        result_df.loc[result_df_row_index, 'answer'] = machine_score_df.loc[row_index, 'answer']
+        result_df.loc[result_df_row_index, 'predict_finetune'] = machine_score_df.loc[row_index, 'predict_finetune']
+        result_df.loc[result_df_row_index, 'predict_origin'] = machine_score_df.loc[row_index, 'predict_origin']
+        result_df.loc[result_df_row_index, 'acc_finetune'] = machine_score_df.loc[row_index, 'acc_finetune']
+        result_df.loc[result_df_row_index, 'human_acc_finetune'] = human_score_df.loc[row_index, '准确度（微调后']
+        result_df.loc[result_df_row_index, 'acc_origin'] = machine_score_df.loc[row_index, 'acc_origin']
+        result_df.loc[result_df_row_index, 'human_acc_origin'] = human_score_df.loc[row_index, '准确度（微调前']
+        result_df.loc[result_df_row_index, 'fluency_finetune'] = machine_score_df.loc[row_index, 'fluency_finetune']
+        result_df.loc[result_df_row_index, 'human_fluency_finetune'] = human_score_df.loc[row_index, '流畅度（微调后']
+        result_df.loc[result_df_row_index, 'diff_score'] = machine_score_df.loc[row_index, 'diff_score']
+        result_df.loc[result_df_row_index, 'human_diff_score'] = human_score_df.loc[row_index, '是否超过原模型']
+        result_df_row_index += 1
+
+
+result_df.to_csv('logs/other/diff.csv', index=False)
+# 信息的准确性应当被首要考虑，多余的未知真假的信息不应该带来加分。

eval.py

@@ -5,26 +5,40 @@ import torch
 from evaluators.chatgpt import ChatGPT_Evaluator
 from evaluators.chatglm import ChatGLM_Evaluator
 from evaluators.chatglm2 import ChatGLM_Evaluator as ChatGLM2_Evaluator
+from evaluators.chatglm3 import ChatGLM_Evaluator as ChatGLM3_Evaluator
 
 import time
 
 choices = ["A", "B", "C", "D"]
+device = torch.device("cpu")
 
 
 def main(args):
+    global device
+    if args.cuda_device:
+        os.environ["CUDA_VISIBLE_DEVICES"] = args.cuda_device
+        device = torch.device("cuda")
     if "turbo" in args.model_name or "gpt-4" in args.model_name:
-        # print("Not supported yet")
-        # return -1
         evaluator = ChatGPT_Evaluator(
             choices=choices,
             k=args.ntrain,
             api_key=args.openai_key,
             model_name=args.model_name
         )
+    elif "chatglm3" in args.model_name:
+        if args.finetune:
+            fine_tune_model = args.finetune
+        else:
+            fine_tune_model = None
+        evaluator = ChatGLM3_Evaluator(
+            choices=choices,
+            k=args.ntrain,
+            model_name=args.model_name,
+            device=device,
+            finetune=fine_tune_model,
+            finetune_method=args.finetune_method
+        )
     elif "chatglm2" in args.model_name:
-        if args.cuda_device:
-            os.environ["CUDA_VISIBLE_DEVICES"] = args.cuda_device
-        device = torch.device("cuda")
         if args.finetune:
             fine_tune_model = args.finetune
         else:
@@ -38,9 +52,6 @@ def main(args):
             finetune_method=args.finetune_method
         )
     elif "chatglm" in args.model_name:
-        if args.cuda_device:
-            os.environ["CUDA_VISIBLE_DEVICES"] = args.cuda_device
-        device = torch.device("cuda")
         if args.finetune:
             fine_tune_model = args.finetune
         else:
@@ -50,7 +61,8 @@ def main(args):
             k=args.ntrain,
             model_name=args.model_name,
             device=device,
-            finetune=fine_tune_model
+            finetune=fine_tune_model,
+            finetune_method=args.finetune_method
         )
     else:
         print("Unknown model name")
@@ -63,33 +75,34 @@ def main(args):
         fine_tune_model_name = args.finetune
     else:
         fine_tune_model_name = 'original'
-    save_result_dir = os.path.join(r"logs", f"{args.model_name}_{fine_tune_model_name}_{run_date}")
+    save_result_dir = os.path.join(r"logs", f"{args.model_name}_{fine_tune_model_name}/{run_date}")
     os.mkdir(save_result_dir)
 
-    subject_list = ['computer_architecture', 'car_knowledge', 'car_use', 'car_market']
+    # subject_list = ['computer_architecture', 'car_knowledge', 'car_use', 'car_market']
+    subject_list = ['car_knowledge_in_train', 'car_use_in_train', 'car_market_in_train']
     qa_subject_list = ['car_knowledge', 'car_use', 'car_market']
-    # qa_subject_list = ['car_use', 'car_market'==
-
-    # for subject_name in subject_list:
-    #     print("Now testing: " + subject_name)
-    #     # subject_name=args.subject
-    #     val_file_path = os.path.join('data/val', f'{subject_name}_val.csv')
-    #     val_df = pd.read_csv(val_file_path)
-    #     if args.few_shot:
-    #         dev_file_path = os.path.join('data/dev', f'{subject_name}_dev.csv')
-    #         dev_df = pd.read_csv(dev_file_path)
-    #         correct_ratio = evaluator.eval_subject(subject_name, val_df, dev_df, few_shot=args.few_shot,
-    #                                                save_result_dir=save_result_dir, cot=args.cot)
-    #     else:
-    #         correct_ratio = evaluator.eval_subject(subject_name, val_df, few_shot=args.few_shot,
-    #                                                save_result_dir=save_result_dir)
-    #     print("Acc:", correct_ratio)
+    # qa_subject_list = ['car_use', 'car_market']
 
-    for subject_name in qa_subject_list:
+    for subject_name in subject_list:
         print("Now testing: " + subject_name)
-        qa_file_path = os.path.join('data/qa', f'{subject_name}_qa.csv')
-        qa_df = pd.read_csv(qa_file_path)
-        evaluator.eval_qa(subject_name, qa_df, save_result_dir=save_result_dir)
+        # subject_name=args.subject
+        val_file_path = os.path.join('data/val', f'{subject_name}_val.csv')
+        val_df = pd.read_csv(val_file_path)
+        if args.few_shot:
+            dev_file_path = os.path.join('data/dev', f'{subject_name}_dev.csv')
+            dev_df = pd.read_csv(dev_file_path)
+            correct_ratio = evaluator.eval_subject(subject_name, val_df, dev_df, few_shot=args.few_shot,
+                                                   save_result_dir=save_result_dir, cot=args.cot)
+        else:
+            correct_ratio = evaluator.eval_subject(subject_name, val_df, few_shot=args.few_shot,
+                                                   save_result_dir=save_result_dir)
+        print("Acc:", correct_ratio)
+
+    # for subject_name in qa_subject_list:
+    #     print("Now testing: " + subject_name)
+    #     qa_file_path = os.path.join('data/qa', f'{subject_name}_qa.csv')
+    #     qa_df = pd.read_csv(qa_file_path)
+    #     evaluator.eval_qa(subject_name, qa_df, save_result_dir=save_result_dir)
 
 
 if __name__ == "__main__":

evaluators/chatglm.py

@@ -17,13 +17,13 @@ class InvalidScoreLogitsProcessor(LogitsProcessor):
 
 
 class ChatGLM_Evaluator(Evaluator):
-    def __init__(self, choices, k, model_name, device, finetune=None):
+    def __init__(self, choices, k, model_name, device, finetune=None, finetune_method=None):
         super(ChatGLM_Evaluator, self).__init__(choices, model_name, k)
         # try adding 'mirror="tuna"' and 'resume_download=True' if facing the 'read timed out' problem
         # or directly clone the model
         self.tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True, mirror="tuna")
-        if finetune:
-            CHECKPOINT_PATH = "ptuning/" + finetune
+        if finetune_method == "ptuning":
+            CHECKPOINT_PATH = "ptuning/glm1/" + finetune
             config = AutoConfig.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True, pre_seq_len=128)
             self.model = AutoModel.from_pretrained("THUDM/chatglm-6b", config=config, trust_remote_code=True)
             prefix_state_dict = torch.load(os.path.join(CHECKPOINT_PATH, "pytorch_model.bin"))
@@ -38,14 +38,12 @@ class ChatGLM_Evaluator(Evaluator):
         else:
             self.model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True, mirror="tuna",
                                                    resume_download=True).half().to(device)
-            print("Model loaded!(GLM)")
+            print("Model loaded! (GLM original)")
 
         # self.model = self.model.eval()
 
     def eval_subject(self, subject_name, test_df, dev_df=None, few_shot=False, cot=False, save_result_dir=None):
         correct_num = 0
-        if save_result_dir:
-            if few_shot:
         result = []
         score = []
         answer_list = []
@@ -62,8 +60,13 @@ class ChatGLM_Evaluator(Evaluator):
                 # For ChatGLM, we use answer extraction in answer-only mode too.
                 ans, direct_extract = self.extract_cot_answer(row, response)
             else:  # zero-shot by extracting answer from distribution
-                ans = self.generate_dist(self.model, self.tokenizer, question, do_sample=False, max_length=2048,
-                                         history=history)
+                response, _ = self.model.chat(self.tokenizer, question, max_length=300,
+                                              do_sample=False, history=history)
+                response = response.strip()
+                ans, direct_extract = self.extract_cot_answer(row, response)
+                # print(response, ans)
+                # ans = self.generate_dist(self.model, self.tokenizer, question, do_sample=False, max_length=2048,
+                #                          history=history)
             if ans == answers[row_index]:
                 correct_num += 1
                 correct = 1

evaluators/chatglm2.py

@@ -26,11 +26,11 @@ class ChatGLM_Evaluator(Evaluator):
         if finetune_method == "lora":
             self.model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True, mirror="tuna",
                                                    resume_download=True).half().to(device)
-            peft_model_id = "lora/" + finetune
+            peft_model_id = "lora/glm2/" + finetune
             self.model = PeftModel.from_pretrained(self.model, peft_model_id)
             print("Model loaded! use GLM2" + finetune)
         elif finetune_method == "ptuning":
-            CHECKPOINT_PATH = "ptuning/" + finetune
+            CHECKPOINT_PATH = "ptuning/glm2/" + finetune
             config = AutoConfig.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True, pre_seq_len=128)
             self.model = AutoModel.from_pretrained("THUDM/chatglm2-6b", config=config, trust_remote_code=True)
             prefix_state_dict = torch.load(os.path.join(CHECKPOINT_PATH, "pytorch_model.bin"))
@@ -55,7 +55,7 @@ class ChatGLM_Evaluator(Evaluator):
         answer_list = []
         if few_shot:
             history = self.generate_few_shot_prompt(subject_name, dev_df, cot=cot)
-            print(history)
+            # print(history)
         else:
             # _ , history = self.model.chat(self.tokenizer, "接下来会提供给你一些选择题，请选出正确的答案。", do_sample=False)
             history = [('接下来会提供给你一些选择题，请选出正确的答案，给出正确的选项即可。', '好的，我会尽力解答。')]

scoring/assessment_engine.py

@@ -25,7 +25,7 @@ class AssessmentEngine:
             rouge_score_sum += rouge_1_f_score
             qa_result_df.loc[row_index, 'rouge_score'] = rouge_1_f_score
             self.gpt_scorer.mode("accuracy")
-            gpt_score_acc, gpt_response_acc = self.gpt_scorer.score_with_chatgpt(test_question,
+            gpt_response_acc, gpt_score_acc = self.gpt_scorer.score_with_chatgpt(test_question,
                                                                                  model_response, reference_answer)
             qa_result_df.loc[row_index, 'gpt_score_acc'] = gpt_score_acc
             qa_result_df.loc[row_index, 'gpt_response_acc'] = gpt_response_acc
@@ -35,3 +35,115 @@ class AssessmentEngine:
         synthesis_score = rouge_score_sum / row_count
         qa_result_df.to_csv('logs/' + self.save_result_dir + '/' + subject_name + '_qa_test_score_'
                             + str(synthesis_score) + '.csv', index=False)
+
+    def eval_result_diff(self, csv_file_name):
+        result_diff_df = pd.read_csv('logs/' + self.save_result_dir + '/' + csv_file_name)
+        result_diff_df['rouge_score_finetune'] = 0
+        result_diff_df['rouge_score_origin'] = 0
+        result_diff_df['acc_finetune'] = 0
+        result_diff_df['acc_origin'] = 0
+        result_diff_df['fluency_finetune'] = 0
+        result_diff_df['fluency_origin'] = 0
+        result_diff_df['diff_score'] = 0
+        result_diff_df['acc_response_finetune'] = 0
+        result_diff_df['acc_response_origin'] = 0
+        result_diff_df['fluency_response_finetune'] = 0
+        result_diff_df['fluency_response_origin'] = 0
+        result_diff_df['diff_score_response'] = 0
+        start_time = time.time()
+        finetune_rouge_score_sum = 0
+        origin_rouge_score_sum = 0
+        finetune_acc_score_sum = 0
+        origin_acc_score_sum = 0
+        finetune_fluency_score_sum = 0
+        origin_fluency_score_sum = 0
+        model_better_score_sum = 0
+        row_count = 0
+        for row_index, row in tqdm(result_diff_df.iterrows(), total=len(result_diff_df)):
+            if row['question'] == '':
+                continue
+            row_count += 1
+            test_question = row['question']
+            finetune_model_response = row['predict_finetune']
+            original_model_response = row['predict_origin']
+            reference_answer = row['answer']
+            # 计算ROUGE分数
+            finetune_rouge_score = get_rouge_score(finetune_model_response, reference_answer)
+            finetune_rouge_1_f_score = finetune_rouge_score['rouge-1']['f']
+            finetune_rouge_score_sum += finetune_rouge_1_f_score
+            result_diff_df.loc[row_index, 'rouge_score_finetune'] = finetune_rouge_1_f_score
+            origin_rouge_score = get_rouge_score(original_model_response, reference_answer)
+            origin_rouge_1_f_score = origin_rouge_score['rouge-1']['f']
+            origin_rouge_score_sum += origin_rouge_1_f_score
+            result_diff_df.loc[row_index, 'rouge_score_origin'] = origin_rouge_1_f_score
+
+            self.gpt_scorer.mode("accuracy")
+            gpt_response_acc, gpt_score_acc = (self.gpt_scorer.score_with_chatgpt(test_question,
+                                                                                  finetune_model_response,
+                                                                                  reference_answer))
+            result_diff_df.loc[row_index, 'acc_finetune'] = gpt_score_acc
+            result_diff_df.loc[row_index, 'acc_response_finetune'] = gpt_response_acc
+            if (gpt_score_acc is not None) and gpt_score_acc.isdigit():
+                finetune_acc_score_sum += float(gpt_score_acc)
+            gpt_response_acc, gpt_score_acc = (self.gpt_scorer.score_with_chatgpt(test_question,
+                                                                                  original_model_response,
+                                                                                  reference_answer))
+            result_diff_df.loc[row_index, 'acc_origin'] = gpt_score_acc
+            result_diff_df.loc[row_index, 'acc_response_origin'] = gpt_response_acc
+            if (gpt_score_acc is not None) and gpt_score_acc.isdigit():
+                origin_acc_score_sum += float(gpt_score_acc)
+
+            self.gpt_scorer.mode("fluency")
+            gpt_response_fluency, gpt_score_fluency = (self.gpt_scorer.score_with_chatgpt(test_question,
+                                                                                          finetune_model_response,
+                                                                                          reference_answer))
+            result_diff_df.loc[row_index, 'fluency_finetune'] = gpt_score_fluency
+            result_diff_df.loc[row_index, 'fluency_response_finetune'] = gpt_response_fluency
+            if (gpt_score_fluency is not None) and gpt_score_fluency.isdigit():
+                finetune_fluency_score_sum += float(gpt_score_fluency)
+            gpt_response_fluency, gpt_score_fluency = (self.gpt_scorer.score_with_chatgpt(test_question,
+                                                                                          original_model_response,
+                                                                                          reference_answer))
+            result_diff_df.loc[row_index, 'fluency_origin'] = gpt_score_fluency
+            result_diff_df.loc[row_index, 'fluency_response_origin'] = gpt_response_fluency
+            if (gpt_score_fluency is not None) and gpt_score_fluency.isdigit():
+                origin_fluency_score_sum += float(gpt_score_fluency)
+
+            self.gpt_scorer.mode("diff")
+            gpt_response_diff, gpt_score_diff = (self.gpt_scorer.score_with_chatgpt(test_question,
+                                                                                    finetune_model_response,
+                                                                                    reference_answer,
+                                                                                    original_model_response))
+            result_diff_df.loc[row_index, 'diff_score'] = gpt_score_diff
+            result_diff_df.loc[row_index, 'diff_score_response'] = gpt_response_diff
+            if (gpt_score_diff is not None) and gpt_score_diff.isdigit():
+                model_better_score_sum += float(gpt_score_diff)
+            result_diff_df.to_csv('logs/' + self.save_result_dir + '/result_diff_test_score_tmp.csv', index=False)
+
+        end_time = time.time()
+        elapsed_time = end_time - start_time
+        print("共评估结果" + str(row_count) + "条，总共用时：", elapsed_time, "秒")
+        synthesis_rouge_score = finetune_rouge_score_sum / row_count
+        original_rouge_score = origin_rouge_score_sum / row_count
+        synthesis_acc_score = finetune_acc_score_sum / row_count
+        original_acc_score = origin_acc_score_sum / row_count
+        synthesis_fluency_score = finetune_fluency_score_sum / row_count
+        original_fluency_score = origin_fluency_score_sum / row_count
+        synthesis_diff_score = model_better_score_sum / row_count
+        print("微调模型ROUGE分数：", synthesis_rouge_score)
+        print("原模型ROUGE分数：", original_rouge_score)
+        print("微调模型准确性分数：", synthesis_acc_score)
+        print("原模型准确性分数：", original_acc_score)
+        print("微调模型流畅度分数：", synthesis_fluency_score)
+        print("原模型流畅度分数：", original_fluency_score)
+        print("微调模型优于原模型分数：", synthesis_diff_score)
+        synthesis_score = (synthesis_rouge_score * 100 + synthesis_acc_score * 100 / 4 +
+                           synthesis_fluency_score * 100 / 3 + synthesis_diff_score * 100 / 3) / 4
+        print("综合评分：", synthesis_score)
+        original_synthesis_score = (original_rouge_score * 100 + original_acc_score * 100 / 4 +
+                                    original_fluency_score * 100 / 3 + 66) / 4
+        print("原模型综合评分：", original_synthesis_score)
+        # 获取当前时间的字符串
+        current_time = time.strftime("%Y%m%d%H%M%S", time.localtime(time.time()))
+        result_diff_df.to_csv('logs/' + self.save_result_dir + '/' + current_time + '_result_diff_test_score_'
+                              + str(synthesis_score) + '.csv', index=False)

scoring/gpt_scorer.py

@@ -8,11 +8,14 @@ class GPTScorer:
         self.eval_mode = "accuracy"
 
     def mode(self, mode):
+        # 判断模式是否合法
+        if mode not in ["accuracy", "fluency", "diff"]:
+            raise ValueError("Invalid mode. Must be one of 'accuracy', 'fluency' or 'diff'.")
         self.eval_mode = mode
         return self
 
-    def score_with_chatgpt(self, question, model_result, reference):
-        prompt = self.generate_scoring_prompt(question, model_result, reference)
+    def score_with_chatgpt(self, question, model_result, reference, origin_model_result=None):
+        prompt = self.generate_scoring_prompt(question, model_result, reference, origin_model_result)
         try:
             # 提交文本以获取ChatGPT评分
             response = openai.ChatCompletion.create(
@@ -25,21 +28,61 @@ class GPTScorer:
             return chatgpt_response, chatgpt_score
         except Exception as e:
             print("An error occurred while scoring with ChatGPT:", e)
-            return None, None
+            return None, '2'
 
-    def generate_scoring_prompt(self, question, model_result, reference):
+    def generate_scoring_prompt(self, question, model_result, reference, origin_model_result=None):
         # 生成评分提示
         base_prompt = []
         if self.eval_mode == "accuracy":
+            # base_prompt = [{
+            #     "role": "system",
+            #     "content": "你是一个汽车领域专家，接下来将向你提供一个问题、一个参考答案和一个大模型生成的结果。"
+            #                "请对比参考答案和大模型生成结果，从信息准确性的角度评分以下生成的结果，以评估其质量。满分为5分。"
+            #                "评分标准为：信息准确无误——5分。信息大致符合实际信息——4分。"
+            #                "信息不全面但明确表达了自身无法回答——3分。信息完全错误——2分。回答无关或回答语句不完整——1分。"
+            #                "可以根据实际情况稍作调整。"
+            #                "回复格式为：评分为x分。理由：xxx。"
+            # }]
             base_prompt = [{
                 "role": "system",
                 "content": "你是一个汽车领域专家，接下来将向你提供一个问题、一个参考答案和一个大模型生成的结果。"
-                           "请对比参考答案和大模型生成结果，从信息准确性的角度评分以下生成的结果，以评估其质量。满分为5分。"
-                           "评分标准为：信息准确无误——5分。信息大致符合实际信息——4分。"
-                           "信息不全面但明确表达了自身无法回答——3分。信息完全错误——2分。回答无关——1分。"
-                           "可以根据实际情况稍作调整。"
-                           "回复格式为：评分为x分。理由：xxx。"
+                           "请对比参考答案和大模型生成结果，从信息准确性的角度评分以下生成的结果，以评估其质量。满分为4分。"
+                           "信息的准确性应当被首要考虑，多余的未知真假的信息不应该带来加分。"
+                           "评分标准为：模型回答正确——4分。模型回答模糊，但部分准确——3分。"
+                           "模型无法给出解答，但明确表示无法解答——2分。模型给出错误或无法理解的回答/模型回答语句不完整——1分。"
+                           "回复格式为：理由：xxx。因此，评分为x分。"
             }]
+        elif self.eval_mode == "fluency":
+            base_prompt = [{
+                "role": "system",
+                "content": "你是一个汽车领域专家，接下来将向你提供一个问题、一个参考答案和一个大模型生成的结果。"
+                           "请从语言流畅度的角度评分大模型生成的结果，以评估其质量。满分为3分。"
+                           "评分标准为：模型回答流畅，符合日常语言习惯——3分。模型回答流畅，但存在突然中断等情况——2分。"
+                           "模型回答无条理，可能重复输出某些单词——1分。"
+                           "回复格式为：理由：xxx。因此，评分为x分。"
+            }]
+        elif self.eval_mode == "diff":
+            base_prompt = [{
+                "role": "system",
+                "content": "你是一个汽车领域专家，接下来将向你提供一个问题、一个参考答案、一个大模型生成的结果和一个微调后大模型生成结果。"
+                           "请对比这些结果，判断微调后大模型的结果是否优于原模型。满分为3分。"
+                           "信息的准确性应当被首要考虑，多余的未知真假的信息不应该带来加分。"
+                           "对比时请关注结果和参考答案的契合度。"
+                           "评分标准为：认为回答优于原模型——3分。认为回答与原模型持平——2分。"
+                           "认为回答不如原模型——1分。"
+                           "回复格式为：理由：xxx。因此，评分为x分。"
+            }]
+        if self.eval_mode == "diff":
+            if origin_model_result is None:
+                raise ValueError("The original model result is required in 'diff' mode.")
+            prompt = base_prompt + [
+                {
+                    "role": "user",
+                    "content": f"问题：{question}\n\n原模型生成的结果：{origin_model_result}\n\n"
+                               f"微调后模型生成的结果：{model_result}\n\n参考答案：{reference}"
+                }
+            ]
+        else:
             prompt = base_prompt + [
                 {
                     "role": "user",
@@ -47,11 +90,13 @@ class GPTScorer:
                 }
             ]
         return prompt
+    # AIzaSyAW_h8itGLwNhYTfx1EDLthhcHHlcIfs7w （google）
 
     def extract_score(self, response_text):
         # 提取评分
         pattern = [
             r"^评分为([1-5])分",
+            r"评分：([1-5])分",
         ]
         score_list = []
         for p in pattern:
@@ -59,6 +104,8 @@ class GPTScorer:
                 score_list = re.findall(p, response_text)
             else:
                 break
+        if len(score_list) == 0:
+            return '3'
         return score_list[0]
 
 

scoring/ppl_scorer.py

@@ -0,0 +1,10 @@
+import evaluate
+
+perplexity = evaluate.load("../metrics/perplexity")
+input_texts = ["你好！", "打死哦对吉萨大你去我家而且我就", "这辆车非常优秀"]
+results = perplexity.compute(model_id='gpt2',
+                             add_start_token=False,
+                             predictions=input_texts)
+print(list(results.keys()))
+print(results["perplexities"])
+

test.py

@@ -1,7 +1,28 @@
 from scoring.assessment_engine import AssessmentEngine
 
-assessment_engine = AssessmentEngine("chatglm2_glm2_pt1_2024-03-08_11-24-47",
+assessment_engine = AssessmentEngine("other",
                                      "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")
-assessment_engine.eval_subject("car_knowledge", "car_knowledge_qa_test_result.csv")
-assessment_engine.eval_subject("car_use", "car_use_qa_test_result.csv")
-assessment_engine.eval_subject("car_market", "car_market_qa_test_result.csv")
+assessment_engine.eval_result_diff("0408output-dora.csv")
+
+# synthesis_rouge_score = 0.30358589506467687
+# print("微调模型ROUGE分数：", synthesis_rouge_score)
+# original_rouge_score = 0.26004000118452175
+# print("原模型ROUGE分数：", original_rouge_score)
+# synthesis_acc_score = 2.768
+# print("微调模型准确性分数：", synthesis_acc_score)
+# original_acc_score = 2.724
+# print("原模型准确性分数：", original_acc_score)
+# synthesis_fluency_score = 2.098
+# print("微调模型流畅度分数：", synthesis_fluency_score)
+# original_fluency_score = 2.236
+# print("原模型流畅度分数：", original_fluency_score)
+# synthesis_diff_score = 2.278
+# print("微调模型优于原模型分数：", synthesis_diff_score)
+#
+# synthesis_score = (synthesis_rouge_score * 100 + synthesis_acc_score * 100/4 + synthesis_fluency_score * 100/3
+#                    + synthesis_diff_score * 100/3 ) / 4
+# original_synthesis_score = (original_rouge_score * 100 + original_acc_score * 100/4 +
+#                             original_fluency_score * 100/3 + 66 ) / 4
+#
+# print("综合评分：", synthesis_score)
+# print("原模型综合评分：", original_synthesis_score)