diff --git a/Project/simple_approach/Continous_Learner.ipynb b/Project/simple_approach/Continous_Learner.ipynb
index 8514c2c..59996ec 100644
--- a/Project/simple_approach/Continous_Learner.ipynb
+++ b/Project/simple_approach/Continous_Learner.ipynb
@@ -144,7 +144,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a4899ee1720f4db4a136a96657f3283a",
+       "model_id": "d304cda50752491da1637b292a9367e8",
        "version_major": 2,
        "version_minor": 0
       },
diff --git a/Project/simple_approach/simple_twitter_learning.py b/Project/simple_approach/simple_twitter_learning.py
index 73ebd5e..88267b3 100644
--- a/Project/simple_approach/simple_twitter_learning.py
+++ b/Project/simple_approach/simple_twitter_learning.py
@@ -641,7 +641,7 @@ class trainer(object):
                 named_steps[k].fit = lambda X, y: named_steps[k].train_on_batch(to_dense_if_sparse(X), y) # ← why has keras no sparse support on batch progressing!?!?!
             
         if batch_size is None:
-            self.pm.fit(X = self.sdm.X[:max_size], y = self.sdm.y[:max_size], validation_split=0.1, epochs=n_epochs)
+            self.pm.fit(X = self.sdm.X[:max_size], y = self.sdm.y[:max_size])
         else:
             n = len(self.sdm.X) // batch_size
             for i in range(n_epochs):