drawing link predictions

predict_links.py

 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
-
-
-# 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
+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 prediction
-linker = neuralproofnet.linker
-categories, links = linker.predict_without_categories(a_s)
-#links = linker.predict_with_categories(a_s, tags_s)
-# 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
 
-if __name__== '__main__':
+      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())
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