diff --git a/.gitignore b/.gitignore
index fc9826b612048b0f6f0d31ab177aa1a95c20c7e2..ac5aa4b67363e07a02541935c18acf2a302b4141 100644
--- a/.gitignore
+++ b/.gitignore
@@ -6,3 +6,4 @@ Utils/gold
 Linker/__pycache__
 Configuration/__pycache__
 __pycache__
+TensorBoard
diff --git a/Linker/Linker.py b/Linker/Linker.py
index 195f8e084e7ab2cbfdd33bed459cd1911bba1cdc..c4069215ed55374c4b035e9ad69553a13a7510a8 100644
--- a/Linker/Linker.py
+++ b/Linker/Linker.py
@@ -81,7 +81,7 @@ class Linker(Module):
 
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-    def __preprocess_data(self, batch_size, df_axiom_links, validation_rate=0.0):
+    def __preprocess_data(self, batch_size, df_axiom_links, validation_rate=0.1):
         r"""
         Args:
             batch_size : int
@@ -106,7 +106,7 @@ class Linker(Module):
         dataset = TensorDataset(atoms_batch_tokenized, atoms_polarity_batch, truth_links_batch, sentences_tokens,
                                 sentences_mask)
 
-        if validation_rate > 0:
+        if validation_rate > 0.0:
             train_size = int(0.9 * len(dataset))
             val_size = len(dataset) - train_size
             train_dataset, val_dataset = random_split(dataset, [train_size, val_size])
diff --git a/train.py b/train.py
index abf0b8072e4cbe9e4ff9aab037bcbb472cd95076..77e89b21a82e4ffd8483ed8295c03b387bdedd4b 100644
--- a/train.py
+++ b/train.py
@@ -6,12 +6,9 @@ from utils import read_csv_pgbar
 
 torch.cuda.empty_cache()
 batch_size = int(Configuration.modelTrainingConfig['batch_size'])
-nb_sentences = batch_size * 20
+nb_sentences = batch_size * 400
 epochs = int(Configuration.modelTrainingConfig['epoch'])
 
-file_path_axiom_links = 'Datasets/goldANDsilver_dataset_links.csv'
-nb_sentences = batch_size * 20
-epochs = int(Configuration.modelTrainingConfig['epoch'])
 
 file_path_axiom_links = 'Datasets/gold_dataset_links.csv'
 df_axiom_links = read_csv_pgbar(file_path_axiom_links, nb_sentences)
@@ -21,10 +18,8 @@ supertagger = SuperTagger()
 supertagger.load_weights("models/flaubert_super_98%_V2_50e.pt")
 
 
-sents_tokenized, sents_mask = supertagger.sent_tokenizer.fit_transform_tensors(sentences_batch)
-
 print("Linker")
 linker = Linker(supertagger)
 linker = linker.to(torch.device("cuda" if torch.cuda.is_available() else "cpu"))
 print("Linker Training")
-linker.train_linker(df_axiom_links, sents_tokenized, sents_mask, validation_rate=0.1, epochs=epochs, batch_size=batch_size, checkpoint=False, validate=True)
+linker.train_linker(df_axiom_links, batch_size=batch_size, checkpoint=False, tensorboard=True)