update

analyse_translation.py

@@ -61,5 +61,5 @@ def export_to_csv(label):
 
 
 if __name__ == "__main__":
-    label = "target_gt"
+    label = "04_commentary_assisted"
     export_to_csv(label)

experiments/04_commentary_assisted.py

@@ -24,13 +24,15 @@ def load_commentaries():
 def generate_prompt(tibetan_text, text_id):
     text_commentaries = commentaries[text_id]
     return f"""
-Translate the Input Buddhist Tibetan text into English using the Tibetan Commentaries provided.
+In order to translate the Input Buddhist Tibetan text into English using Tibetan commentaries, do the following:
+1. Translate and summarize each commentary
+2. Combine the meaning of the commentaries
+3. Translate the Input text in accordance to the combine meaning of the commentaries.
 
-## Core Instructions
-   - Enclose final English translation in <t> tags
+Core Instructions
+Enclose final English translation in <t> tags
 
-Input:
-{tibetan_text}
+Input: {tibetan_text}
 
 Commentary 1: {text_commentaries[0]}
 
@@ -42,6 +44,7 @@ def generate_prompt(tibetan_text, text_id):
 if __name__ == "__main__":
     arg_parser = argparse.ArgumentParser()
     arg_parser.add_argument("--test", action="store_true")
+    arg_parser.add_argument("--replace", action="store_true")
     args = arg_parser.parse_args()
 
     exp_name = Path(__file__).stem
@@ -52,4 +55,4 @@ def generate_prompt(tibetan_text, text_id):
         claud_sonet_chat,
         generate_prompt,
     )
-    exp.run_experiment(test=args.test)
+    exp.run_experiment(replace=args.replace, test=args.test)

experiments/experiment.py

@@ -46,9 +46,9 @@ def is_translated(self, text_id):
             and self.exp_name in results[text_id]["target_pred"]
         )
 
-    def run_experiment(self, test=False):
+    def run_experiment(self, replace=False, test=False):
         for text_id, source_text in self.get_source_texts():
-            if self.is_translated(text_id):
+            if not replace and self.is_translated(text_id):
                 continue
             prompt = self.prompt_generator(source_text, text_id)
             response = self.llm(prompt)