diff --git a/label_studio_ml/examples/bert_classifier/model.py b/label_studio_ml/examples/bert_classifier/model.py
index d5bc5f6e..f0c54a91 100644
--- a/label_studio_ml/examples/bert_classifier/model.py
+++ b/label_studio_ml/examples/bert_classifier/model.py
@@ -122,7 +122,8 @@ def fit(self, event, data, **additional_params):
         if event not in ('ANNOTATION_CREATED', 'ANNOTATION_UPDATED', 'START_TRAINING'):
             logger.info(f"Skip training: event {event} is not supported")
             return
-        project_id = data['annotation']['project']
+        logger.debug(f"Project details payload for training: {data}")
+        project_id = data['project']['id']
 
         # dowload annotated tasks from Label Studio
         ls = label_studio_sdk.Client(self.LABEL_STUDIO_HOST, self.LABEL_STUDIO_API_KEY)
diff --git a/label_studio_ml/examples/huggingface_ner/model.py b/label_studio_ml/examples/huggingface_ner/model.py
index 5e89f8a8..74aee430 100644
--- a/label_studio_ml/examples/huggingface_ner/model.py
+++ b/label_studio_ml/examples/huggingface_ner/model.py
@@ -52,7 +52,7 @@ def get_labels(self):
         from_name, _, _ = li.get_first_tag_occurence('Labels', 'Text')
         tag = li.get_tag(from_name)
         return tag.labels
-    
+
     def setup(self):
         """Configure any paramaters of your model here
         """
@@ -102,7 +102,7 @@ def predict(self, tasks: List[Dict], context: Optional[Dict] = None, **kwargs) -
                     'score': avg_score / len(results),
                     'model_version': self.get('model_version')
                 })
-        
+
         return ModelResponse(predictions=predictions, model_version=self.get('model_version'))
 
     def _get_tasks(self, project_id):
@@ -135,7 +135,7 @@ def tokenize_and_align_labels(self, examples, tokenizer):
 
         tokenized_inputs["labels"] = labels
         return tokenized_inputs
-    
+
     def fit(self, event, data, **kwargs):
         """Download dataset from Label Studio and prepare data for training in BERT
         """
@@ -143,7 +143,8 @@ def fit(self, event, data, **kwargs):
             logger.info(f"Skip training: event {event} is not supported")
             return
 
-        project_id = data['annotation']['project']
+        logger.debug(f"Project details payload for training: {data}")
+        project_id = data['project']['id']
         tasks = self._get_tasks(project_id)
 
         if len(tasks) % self.START_TRAINING_EACH_N_UPDATES != 0 and event != 'START_TRAINING':
diff --git a/label_studio_ml/examples/sklearn_text_classifier/model.py b/label_studio_ml/examples/sklearn_text_classifier/model.py
index 78adbac6..c162d350 100644
--- a/label_studio_ml/examples/sklearn_text_classifier/model.py
+++ b/label_studio_ml/examples/sklearn_text_classifier/model.py
@@ -74,7 +74,7 @@ def get_label_studio_parameters(self) -> Dict:
             'value': value,
             'labels': labels
         }
-        
+
     def predict(self, tasks: List[Dict], context: Optional[Dict] = None, **kwargs) -> ModelResponse:
         """
         This method is used to predict the labels for a given list of tasks.
@@ -162,7 +162,8 @@ def fit(self, event, data, **kwargs):
             logger.info(f"Skip training: event {event} is not supported")
             return
 
-        project_id = data['annotation']['project']
+        logger.debug(f"Project details payload for training: {data}")
+        project_id = data['project']['id']
         tasks = self._get_tasks(project_id)
 
         # Get the labeling configuration parameters like labels and input / output annotation format names