update

Linker/Linker.py

@@ -380,6 +380,9 @@ class Linker(Module):
         Args :
             sentence : list of words composing the sentence
             categories : list of categories (tags) of each word
+
+        Return :
+            links : links prediction
         """
         self.eval()
         with torch.no_grad():
@@ -413,6 +416,10 @@ class Linker(Module):
 
         Args :
             sentence : list of words composing the sentence
+
+        Return :
+            categories : the supertags predicted
+            links : links prediction
         """
         self.eval()
         with torch.no_grad():
@@ -440,7 +447,7 @@ class Linker(Module):
             logits_predictions = self(num_atoms_per_word, atoms_tokenized, pos_idx, neg_idx, output['word_embeding'])
             axiom_links_pred = torch.argmax(logits_predictions, dim=3)
 
-        return axiom_links_pred
+        return categories, axiom_links_pred
 
     def load_weights(self, model_file):
         print("#" * 15)

README.md

@@ -15,6 +15,35 @@ Clone the project locally.
 
 Run the init.sh script or install the Tagger project under SuperTagger name. And upload the tagger.pt in the directory 'models'. (You may need to modify 'model_tagger' in train.py.)
 
+### Structure
+
+The structure should look like this : 
+```
+.
+.
+├── Configuration                    # Configuration
+│   ├── Configuration.py             # Contains the function to execute for config
+│   └── config.ini                   # contains parameters
+├── find_config.py                   # auto-configurate datasets parameters (max length sentence etc) according to the dataset given
+├── requirements.txt                 # librairies needed
+├── Datasets                         # TLGbank data with links
+├── SuperTagger                      # The Supertagger directory (that you need to install)
+│    ├── Datasets                    # TLGbank data
+│    ├── SuperTagger                 # Implementation of BertForTokenClassification
+│    │   ├── SuperTagger.py          # Main class
+│    │   └── Tagging_bert_model.py   # Bert model
+│    ├── predict.py                  # Example of prediction for supertagger
+│    └── train.py                    # Example of train for supertagger
+├── Linker                           # The Linker directory
+│    ├── ...
+│    └── Linker.py                   # Linker class containing the neural network
+├── models                           
+│    └── supertagger.pt              # the pt file contaning the pretrained supertagger (you need to install it)    
+├── Output                           # Directory where your linker models will be savec if checkpoint=True in train               
+├── TensorBoard                      # Directory where the stats will be savec if tensorboard=True in train
+└──  train.py                        # Example of train
+```
+
 ### Dataset format
 
 The sentences should be in a column "X", the links with '_x' postfix should be in a column "Y" and the categories in a column "Z".
@@ -24,8 +53,8 @@ For the links each atom_x goes with the one and only other atom_x in the sentenc
 
 Launch train.py, if you look at it you can give another dataset file and another tagging model.
 
-In train, if you use `checkpoint=True`, the model is automatically saved in a folder: Training_XX-XX_XX-XX. It saves
-after each epoch. Use `tensorboard=True` for log in same folder. (`tensorboard --logdir=logs` for see logs)
+In train, if you use `checkpoint=True`, the model is automatically saved in a folder: Output/Training_XX-XX_XX-XX. It saves
+after each epoch. Use `tensorboard=True` for log saving in folder TensorBoard. (`tensorboard --logdir=logs` for see logs)
 
 ## Predicting
 

postprocessing.py

@@ -77,7 +77,7 @@ def draw_sentence_output(sentence, categories, links):
     Drawing the prediction of a sentence when given categories and links predictions
     :param sentence: list of words
     :param categories: list of categories
-    :param links: links predicted
+    :param links: links predicted, output of predict_with/without_categories
     :return: dot source
     """
     dot = graphviz.Graph('linking', comment='Axiom linking')