diff --git a/delft/applications/softwareContextClassifier.py b/delft/applications/softwareContextClassifier.py
index 7db257fe..c0dc8b47 100644
--- a/delft/applications/softwareContextClassifier.py
+++ b/delft/applications/softwareContextClassifier.py
@@ -40,7 +40,7 @@ def configure(architecture):
     if architecture == "bert":
         batch_size = 32
         early_stop = False
-        max_epoch = 5
+        max_epoch = 6
         maxlen = 100
 
     return batch_size, maxlen, patience, early_stop, max_epoch
@@ -99,6 +99,35 @@ def train_and_eval(embeddings_name, fold_count, architecture="gru", transformer=
     model.save()
 
 
+def train_binary(embeddings_name, fold_count, architecture="gru", transformer=None):
+    print('loading multiclass software context dataset...')
+    x_train, y_train = load_software_context_corpus_json("data/textClassification/software/software-contexts.json.gz")
+
+    report_training_contexts(y_train)
+
+    for class_rank in range(len(list_classes)):
+        model_name = 'software_context_' + list_classes[class_rank] + '_'+architecture
+        class_weights = None
+
+        batch_size, maxlen, patience, early_stop, max_epoch = configure(architecture)
+
+        y_train_class_rank = [ [1, 0] if y[class_rank] == 1.0 else [0, 1] for y in y_train ]
+        y_train_class_rank = np.array(y_train_class_rank)
+
+        list_classes_rank = [list_classes[class_rank], "not_"+list_classes[class_rank]]
+
+        model = Classifier(model_name, architecture=architecture, list_classes=list_classes_rank, max_epoch=max_epoch, fold_number=fold_count, patience=patience,
+            use_roc_auc=True, embeddings_name=embeddings_name, batch_size=batch_size, maxlen=maxlen, early_stop=early_stop,
+            class_weights=class_weights, transformer_name=transformer)
+
+        if fold_count == 1:
+            model.train(x_train, y_train_class_rank)
+        else:
+            model.train_nfold(x_train, y_train_class_rank)
+        # saving the model
+        model.save()
+
+
 def train_and_eval_binary(embeddings_name, fold_count, architecture="gru", transformer=None): 
     print('loading multiclass software context dataset...')
     xtr, y = load_software_context_corpus_json("data/textClassification/software/software-contexts.json.gz")
@@ -203,8 +232,8 @@ def report_training_contexts(y):
 
     args = parser.parse_args()
 
-    if args.action not in ('train', 'train_eval', 'classify'):
-        print('action not specified, must be one of [train,train_eval,classify]')
+    if args.action not in ('train', 'train_eval', 'classify', 'train_binary', 'train_eval_binary'):
+        print('action not specified, must be one of [train,train_binary,train_eval,train_eval_binary,classify]')
 
     embeddings_name = args.embedding
     transformer = args.transformer
@@ -223,6 +252,12 @@ def report_training_contexts(y):
 
         train(embeddings_name, args.fold_count, architecture=architecture, transformer=transformer)
 
+    if args.action == 'train_binary':
+        if args.fold_count < 1:
+            raise ValueError("fold-count should be equal or more than 1")
+
+        train_binary(embeddings_name, args.fold_count, architecture=architecture, transformer=transformer)
+
     if args.action == 'train_eval':
         if args.fold_count < 1:
             raise ValueError("fold-count should be equal or more than 1")
diff --git a/setup.py b/setup.py
index e035e845..cbcfd928 100644
--- a/setup.py
+++ b/setup.py
@@ -27,7 +27,8 @@
         'truecase',
         'requests',
         'pandas==1.3.5',
-        'pytest'
+        'pytest',
+        'tensorflow-addons==0.15.0'  
     ],
     classifiers=[
         "Programming Language :: Python :: 3.7",