diff --git a/Linker/Linker.py b/Linker/Linker.py
index e8494c9adf97d9cb9bbed7784901eb22ed32154f..5002f9c7742b545b9dcf445558b44441ff182b8c 100644
--- a/Linker/Linker.py
+++ b/Linker/Linker.py
@@ -440,7 +440,7 @@ class Linker(Module):
         with torch.no_grad():
             self.cpu()
             self.device = torch.device("cpu")
-            sentences_tokens, sentences_mask = self.Supertagger.sent_tokenizer.fit_transform_tensors([sentence])
+            sentences_tokens, sentences_mask = self.Supertagger.sent_tokenizer.fit_transform_tensors(sentence)
             nb_sentence, len_sentence = sentences_tokens.shape
 
             hidden_state, categories = self.Supertagger.predict(sentence)
diff --git a/postprocessing.py b/postprocessing.py
index d2d43f03a99f877de7391c9e62bd91aea2996e30..e60c0a035667c57d47b9abc57ef1ba87b3bf6a2f 100644
--- a/postprocessing.py
+++ b/postprocessing.py
@@ -113,7 +113,8 @@ def draw_sentence_output(sentence, categories, links):
     return dot.source
 
 
-sentence = ["Le", "chat", "est", "noir", "bleu"]
-categories = ["dr(0,s,n)", "dl(0,s,n)", "dr(0,dl(0,n,np),n)", "dl(0,np,n)", "n"]
-links = np.array([[0, 0, 0, 0], [0, 0, 0, 0], [1, 0, 2, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]])
-draw_sentence_output(sentence, categories, links)
+if __name__== '__main__':
+    sentence = ["Le", "chat", "est", "noir", "bleu"]
+    categories = ["dr(0,s,n)", "dl(0,s,n)", "dr(0,dl(0,n,np),n)", "dl(0,np,n)", "n"]
+    links = np.array([[0, 0, 0, 0], [0, 0, 0, 0], [1, 0, 2, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]])
+    draw_sentence_output(sentence, categories, links)
diff --git a/predict_links.py b/predict_links.py
index f3d05a640791236f93eefff56263369db59384d9..4e87a1f5fae1892530e929dbf778e648531fa8dc 100644
--- a/predict_links.py
+++ b/predict_links.py
@@ -1,26 +1,25 @@
 from NeuralProofNet.NeuralProofNet import NeuralProofNet
+from postprocessing import draw_sentence_output
 
-# region data
-a_s = "( 1 ) parmi les huit \" partants \" acquis ou potentiels , MM. Lacombe , Koehler et Laroze ne sont pas membres " \
-      "du PCF . "
-tags_s = ['let', 'dr(0,s,s)', 'let', 'dr(0,dr(0,s,s),np)', 'dr(0,np,n)', 'dr(0,n,n)', 'let', 'n', 'let', 'dl(0,n,n)',
-           'dr(0,dl(0,dl(0,n,n),dl(0,n,n)),dl(0,n,n))', 'dl(0,n,n)', 'let', 'dr(0,np,np)', 'np', 'dr(0,dl(0,np,np),np)',
-           'np', 'dr(0,dl(0,np,np),np)', 'np', 'dr(0,dl(0,np,s),dl(0,np,s))', 'dr(0,dl(0,np,s),np)', 'dl(1,s,s)', 'np',
-           'dr(0,dl(0,np,np),n)', 'n', 'dl(0,s,txt)']
-# endregion
+if __name__== '__main__':
+      # region data
+      a_s = ["( 1 ) parmi les huit \" partants \" acquis ou potentiels , MM. Lacombe , Koehler et Laroze ne sont pas membres du PCF ."]
+      tags_s = ['let', 'dr(0,s,s)', 'let', 'dr(0,dr(0,s,s),np)', 'dr(0,np,n)', 'dr(0,n,n)', 'let', 'n', 'let', 'dl(0,n,n)',
+            'dr(0,dl(0,dl(0,n,n),dl(0,n,n)),dl(0,n,n))', 'dl(0,n,n)', 'let', 'dr(0,np,np)', 'np', 'dr(0,dl(0,np,np),np)',
+            'np', 'dr(0,dl(0,np,np),np)', 'np', 'dr(0,dl(0,np,s),dl(0,np,s))', 'dr(0,dl(0,np,s),np)', 'dl(1,s,s)', 'np',
+            'dr(0,dl(0,np,np),n)', 'n', 'dl(0,s,txt)']
+      # endregion
 
 
-# region model
-model_tagger = "models/flaubert_super_98_V2_50e.pt"
-neuralproofnet = NeuralProofNet(model_tagger)
-model = "Output/linker.pt"
-neuralproofnet.linker.load_weights(model)
-# endregion
+      # region model
+      model_tagger = "models/flaubert_super_98_V2_50e.pt"
+      neuralproofnet = NeuralProofNet(model_tagger)
+      model = "Output/linker.pt"
+      neuralproofnet.linker.load_weights(model)
+      # endregion
 
-
-# region prediction
-linker = neuralproofnet.linker
-links = linker.predict_without_categories(a_s)
-#links = linker.predict_with_categories(a_s, tags_s)
-print(links)
-# endregion
\ No newline at end of file
+      linker = neuralproofnet.linker
+      categories, links = linker.predict_without_categories(a_s)
+      #links = linker.predict_with_categories(a_s, tags_s)
+      idx=0
+      draw_sentence_output(a_s[idx].split(" "), categories[idx], links[:,idx,:].numpy())
diff --git a/predict_supertags.py b/predict_supertags.py
index 88ec00460056c7e8e21200de33a3dc89798c57a5..6da9f075b99cd3937c448d0ebc3aafd0a8ca88e9 100644
--- a/predict_supertags.py
+++ b/predict_supertags.py
@@ -2,12 +2,8 @@ from SuperTagger.SuperTagger.SuperTagger import SuperTagger
 from SuperTagger.SuperTagger.eval import categorical_accuracy
 
 # region data
-a_s = "( 1 ) parmi les huit \" partants \" acquis ou potentiels , MM. Lacombe , Koehler et Laroze ne sont pas membres " \
-      "du PCF . "
-tags_s = [['let', 'dr(0,s,s)', 'let', 'dr(0,dr(0,s,s),np)', 'dr(0,np,n)', 'dr(0,n,n)', 'let', 'n', 'let', 'dl(0,n,n)',
-           'dr(0,dl(0,dl(0,n,n),dl(0,n,n)),dl(0,n,n))', 'dl(0,n,n)', 'let', 'dr(0,np,np)', 'np', 'dr(0,dl(0,np,np),np)',
-           'np', 'dr(0,dl(0,np,np),np)', 'np', 'dr(0,dl(0,np,s),dl(0,np,s))', 'dr(0,dl(0,np,s),np)', 'dl(1,s,s)', 'np',
-           'dr(0,dl(0,np,np),n)', 'n', 'dl(0,s,txt)']]
+a_s = "85,6 % de membres du PCF ."
+tags_s = [['dr(0,np,n)', 'n', 'dr(0,dl(0,np,np),n)', 'n', 'dr(0,dl(0,n,n),n)', 'n', 'dl(0,np,txt)']]
 # endregion