Change to TemplateNLG

ConvLab-3

@@ -0,0 +1 @@
+Subproject commit 60f4e5641f93e99b8d61b49cf5fd6dc818a83c4c

MachineLearningNLG.py

@@ -33,15 +33,14 @@ class MachineLearningNLG:
         return translated_response
 
     def generate(self, action):
-        # Przyjmujemy, że 'action' jest formatowanym stringiem, który jest przekazywany do self.nlg
         return self.nlg(action)
 
     def init_session(self):
-        pass  # Dodanie pustej metody init_session
+        pass
 
 
 # Przykład użycia
 if __name__ == "__main__":
     nlg = MachineLearningNLG()
-    system_act = "inform(date.from=15.07, date.to=22.07)"
+    system_act = "inform(hotel='Four Seasons Hotel')"
     print(nlg.nlg(system_act))

Main.py

@@ -3,13 +3,14 @@ from DialoguePolicy import DialoguePolicy
 from DialogueStateTracker import DialogueStateTracker
 from convlab.dialog_agent import PipelineAgent
 from MachineLearningNLG import MachineLearningNLG  # Importujemy nowy komponent NLG
+from convlab.nlg.template.multiwoz import TemplateNLG
 
 if __name__ == "__main__":
     text = "chciałbym zarezerwować drogi hotel z parkingiem 1 stycznia w Warszawie w centrum"
     nlu = NaturalLanguageAnalyzer()
     dst = DialogueStateTracker()
     policy = DialoguePolicy()
-    nlg = MachineLearningNLG()
+    nlg = TemplateNLG(is_user=False)
 
     agent = PipelineAgent(nlu=nlu, dst=dst, policy=policy, nlg=nlg, name='sys')
     response = agent.response(text)

train_nlg.py

@@ -11,7 +11,8 @@ for file_name in os.listdir(translated_data_directory):
     if file_name.endswith('.tsv'):
         file_path = os.path.join(translated_data_directory, file_name)
         df = pd.read_csv(file_path, sep='\t')
-        dfs.append(df)
+        df_user = df[df['role'] == 'system'].drop('role', axis=1)
+        dfs.append(df_user)
 combined_df = pd.concat(dfs, ignore_index=True)
 
 # Przygotowanie zbioru danych do trenowania
@@ -49,7 +50,7 @@ training_args = Seq2SeqTrainingArguments(
     per_device_eval_batch_size=16,
     predict_with_generate=True,
     learning_rate=5e-5,
-    num_train_epochs=3,
+    num_train_epochs=10,
     evaluation_strategy="epoch",
     save_strategy="epoch",
     save_total_limit=None,  # Wyłącz rotację punktów kontrolnych