epochs = 3

docker-compose.yml

@@ -21,7 +21,7 @@ services:
       # - Lower (1): Faster training but might underfit
       # - Higher (3+): Better learning but might overfit
       # - Default (2): Good balance for small datasets
-      - NUM_TRAIN_EPOCHS=100
+      - NUM_TRAIN_EPOCHS=3
       
       # Prevents model weights from growing too large (regularization)
       # - Lower (0.001): Less regularization, might overfit

model.py

@@ -64,7 +64,7 @@ class BertClassifier(LabelStudioMLBase):
         
         # Load training configuration from environment variables with defaults
         self.learning_rate = float(os.getenv('LEARNING_RATE', '2e-5'))
-        self.num_train_epochs = int(os.getenv('NUM_TRAIN_EPOCHS', '100'))
+        self.num_train_epochs = int(os.getenv('NUM_TRAIN_EPOCHS', '3'))
         self.weight_decay = float(os.getenv('WEIGHT_DECAY', '0.01'))
         self.start_training_threshold = int(os.getenv('START_TRAINING_EACH_N_UPDATES', '1'))