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4 Commits
master ... test_branc

Author SHA1 Message Date
PawelDopierala
893733025b Change to TemplateNLG 2024-06-04 13:07:41 +02:00
s464786
99306f0532 Merge remote-tracking branch 'origin/test_branch' into test_branch 2024-06-04 11:44:58 +02:00
s464786
7a39f24359 test_learning 2024-06-04 11:43:14 +02:00
Maciej Matusz
7306fdb83a test- commit 2024-06-04 10:56:40 +02:00
4 changed files with 8 additions and 6 deletions
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1
ConvLab-3 Submodule

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

5
MachineLearningNLG.py
View File

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

3
Main.py
View File

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

5
train_nlg.py
View File

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