diff --git a/nxontology_ml/model/predict.py b/nxontology_ml/model/predict.py
index 6fb4254..1365221 100644
--- a/nxontology_ml/model/predict.py
+++ b/nxontology_ml/model/predict.py
@@ -52,6 +52,7 @@ def predict(
     take: int | None = 0,
 ) -> pd.DataFrame:
     target_nodes: list[str] = list(get_disease_nodes(take=take, nxo=nxo))
+    assert len(target_nodes) > 0, "No disease node found"
     target_features = feature_pipeline.transform(target_nodes)
     target_labels = model.predict(target_features)
     target_probas = model.predict_proba(target_features)
diff --git a/nxontology_ml/sklearn_transformer.py b/nxontology_ml/sklearn_transformer.py
index 1095d80..1cd0308 100644
--- a/nxontology_ml/sklearn_transformer.py
+++ b/nxontology_ml/sklearn_transformer.py
@@ -8,6 +8,7 @@
 from nxontology.node import NodeInfo
 from pandas.core.dtypes.base import ExtensionDtype
 from sklearn.base import TransformerMixin
+from tqdm import tqdm
 
 
 @dataclass
@@ -73,16 +74,32 @@ def transform(self, X: NodeFeatures, copy: bool | None = None) -> NodeFeatures:
             return X
         if self._num_features_fn:
             assert self._num_features_names
+
+            vecs: list[np.array] = []
+            for node in tqdm(
+                X.nodes,
+                desc=f"{self.__class__.__name__}: Computing num features",
+                delay=5,
+            ):
+                vecs.append(self._num_features_fn(node))
+
             new_features = pd.DataFrame(
-                data=[self._num_features_fn(node) for node in X.nodes],
+                data=vecs,
                 columns=self._num_features_names,
                 dtype=self._num_feature_dtype,
             )
             X.num_features = pd.concat([X.num_features, new_features], axis=1)
         if self._cat_features_fn:
             assert self._cat_features_names
+            cat_vecs: list[np.array] = []
+            for node in tqdm(
+                X.nodes,
+                desc=f"{self.__class__.__name__}: Computing cat features",
+                delay=5,
+            ):
+                cat_vecs.append(self._cat_features_fn(node))
             new_features = pd.DataFrame(
-                data=[self._cat_features_fn(node) for node in X.nodes],
+                data=cat_vecs,
                 columns=self._cat_features_names,
             )
             X.cat_features = pd.concat([X.cat_features, new_features], axis=1)
diff --git a/nxontology_ml/text_embeddings/text_embeddings_transformer.py b/nxontology_ml/text_embeddings/text_embeddings_transformer.py
index ada34b9..f788272 100644
--- a/nxontology_ml/text_embeddings/text_embeddings_transformer.py
+++ b/nxontology_ml/text_embeddings/text_embeddings_transformer.py
@@ -3,6 +3,7 @@
 from sklearn.base import TransformerMixin
 from sklearn.decomposition import PCA
 from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
+from tqdm import tqdm
 
 from nxontology_ml.sklearn_transformer import (
     NodeFeatures,
@@ -77,7 +78,15 @@ def transform(self, X: NodeFeatures, copy: bool | None = None) -> NodeFeatures:
         return X
 
     def _nodes_to_vec(self, X: NodeFeatures) -> np.ndarray:
-        return np.array([self._embedding_model.embed_node(node) for node in X.nodes])
+        embedded_nodes: list[np.array] = []
+        for node in tqdm(
+            X.nodes,
+            desc="Fetching node embeddings",
+            delay=5,
+        ):
+            embedded_nodes.append(self._embedding_model.embed_node(node))
+
+        return np.array(embedded_nodes)
 
     @classmethod
     def from_config(