update scripts

src/backend/evaluate_model.py

@@ -85,8 +85,18 @@ class Evaluator:
             # exit()
             # avg_summary_len = self.summary_generator.avg_length
             # answer_rate = self.summary_generator.answer_rate
+            envs.API.upload_file(
+                path_or_fileobj=f"./generation_results/{self.model}.csv",
+                path_in_repo=f"{self.model}.csv",
+                repo_id=envs.RESULTS_REPO,
+                repo_type="dataset",
+            )
+
             '''开始评估模型的结果'''
             self.humanlike = self.eval_model.evaluate_humanlike(self.generated_summaries_df, envs.HUMAN_DATA, f"./generation_results/{self.model}.csv")
+
+
+
             '''原始指标'''
             # self.hallucination_scores, self.eval_results = self.eval_model.evaluate_hallucination(
                 # self.generated_summaries_df)

src/backend/model_operations.py

@@ -187,7 +187,7 @@ class SummaryGenerator:
                     # print(ID, q_ID, prompt_value)
                     system_prompt = envs.SYSTEM_PROMPT 
                     _user_prompt = prompt_value
-                    for ii in range(1):
+                    for ii in range(10):
                     # user_prompt = f"{envs.USER_PROMPT}\nPassage:\n{_source}"
                         while True:
                             try:
@@ -1242,6 +1242,14 @@ class EvaluationModel:
         '''coding llm data'''
         save_path = result_save_path.replace('.csv','_coding.csv')
         self.llm_df = self.code_results_llm(summaries_df)
+
+        envs.API.upload_file(
+            path_or_fileobj=f"./generation_results/{self.model}_coding.csv",
+            path_in_repo=f"{self.model}_coding.csv",
+            repo_id=envs.RESULTS_REPO,
+            repo_type="dataset",
+        )
+
         if save_path is not None:
             print(f'Save LLM coding results to {save_path}')
             fpath = Path(save_path)