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test_branc
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1
ConvLab-3
Submodule
1
ConvLab-3
Submodule
@ -0,0 +1 @@
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Subproject commit 60f4e5641f93e99b8d61b49cf5fd6dc818a83c4c
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@ -33,15 +33,14 @@ class MachineLearningNLG:
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return translated_response
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return translated_response
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def generate(self, action):
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def generate(self, action):
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# Przyjmujemy, że 'action' jest formatowanym stringiem, który jest przekazywany do self.nlg
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return self.nlg(action)
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return self.nlg(action)
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def init_session(self):
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def init_session(self):
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pass # Dodanie pustej metody init_session
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pass
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# Przykład użycia
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# Przykład użycia
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if __name__ == "__main__":
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if __name__ == "__main__":
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nlg = MachineLearningNLG()
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nlg = MachineLearningNLG()
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system_act = "inform(date.from=15.07, date.to=22.07)"
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system_act = "inform(hotel='Four Seasons Hotel')"
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print(nlg.nlg(system_act))
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print(nlg.nlg(system_act))
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3
Main.py
3
Main.py
@ -3,13 +3,14 @@ from DialoguePolicy import DialoguePolicy
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from DialogueStateTracker import DialogueStateTracker
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from DialogueStateTracker import DialogueStateTracker
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from convlab.dialog_agent import PipelineAgent
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from convlab.dialog_agent import PipelineAgent
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from MachineLearningNLG import MachineLearningNLG # Importujemy nowy komponent NLG
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from MachineLearningNLG import MachineLearningNLG # Importujemy nowy komponent NLG
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from convlab.nlg.template.multiwoz import TemplateNLG
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if __name__ == "__main__":
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if __name__ == "__main__":
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text = "chciałbym zarezerwować drogi hotel z parkingiem 1 stycznia w Warszawie w centrum"
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text = "chciałbym zarezerwować drogi hotel z parkingiem 1 stycznia w Warszawie w centrum"
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nlu = NaturalLanguageAnalyzer()
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nlu = NaturalLanguageAnalyzer()
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dst = DialogueStateTracker()
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dst = DialogueStateTracker()
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policy = DialoguePolicy()
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policy = DialoguePolicy()
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nlg = MachineLearningNLG()
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nlg = TemplateNLG(is_user=False)
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agent = PipelineAgent(nlu=nlu, dst=dst, policy=policy, nlg=nlg, name='sys')
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agent = PipelineAgent(nlu=nlu, dst=dst, policy=policy, nlg=nlg, name='sys')
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response = agent.response(text)
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response = agent.response(text)
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@ -11,7 +11,8 @@ for file_name in os.listdir(translated_data_directory):
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if file_name.endswith('.tsv'):
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if file_name.endswith('.tsv'):
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file_path = os.path.join(translated_data_directory, file_name)
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file_path = os.path.join(translated_data_directory, file_name)
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df = pd.read_csv(file_path, sep='\t')
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df = pd.read_csv(file_path, sep='\t')
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dfs.append(df)
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df_user = df[df['role'] == 'system'].drop('role', axis=1)
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dfs.append(df_user)
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combined_df = pd.concat(dfs, ignore_index=True)
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combined_df = pd.concat(dfs, ignore_index=True)
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# Przygotowanie zbioru danych do trenowania
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# Przygotowanie zbioru danych do trenowania
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@ -49,7 +50,7 @@ training_args = Seq2SeqTrainingArguments(
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per_device_eval_batch_size=16,
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per_device_eval_batch_size=16,
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predict_with_generate=True,
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predict_with_generate=True,
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learning_rate=5e-5,
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learning_rate=5e-5,
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num_train_epochs=3,
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num_train_epochs=10,
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evaluation_strategy="epoch",
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evaluation_strategy="epoch",
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save_strategy="epoch",
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save_strategy="epoch",
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save_total_limit=None, # Wyłącz rotację punktów kontrolnych
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save_total_limit=None, # Wyłącz rotację punktów kontrolnych
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