diff --git a/README.md b/README.md
index 1c54594..934011b 100644
--- a/README.md
+++ b/README.md
@@ -101,7 +101,7 @@ embeddings = model.get_embeddings()# get embedding vectors
 ```python
 G = nx.read_edgelist('../data/flight/brazil-airports.edgelist',create_using=nx.DiGraph(),nodetype=None,data=[('weight',int)])#read graph
 
-model = model = Struc2Vec(G, 10, 80, workers=4, verbose=40, ) #init model
+model = Struc2Vec(G, 10, 80, workers=4, verbose=40, ) #init model
 model.train(window_size = 5, iter = 3)# train model
 embeddings = model.get_embeddings()# get embedding vectors
 ```
diff --git a/ge/classify.py b/ge/classify.py
index eb2bc67..6381284 100644
--- a/ge/classify.py
+++ b/ge/classify.py
@@ -5,6 +5,7 @@
 from sklearn.metrics import f1_score, accuracy_score
 from sklearn.multiclass import OneVsRestClassifier
 from sklearn.preprocessing import MultiLabelBinarizer
+import pandas as pd
 
 
 class TopKRanker(OneVsRestClassifier):
@@ -69,17 +70,9 @@ def split_train_evaluate(self, X, Y, train_precent, seed=0):
 
 
 def read_node_label(filename, skip_head=False):
-    fin = open(filename, 'r')
-    X = []
-    Y = []
-    while 1:
-        if skip_head:
-            fin.readline()
-        l = fin.readline()
-        if l == '':
-            break
-        vec = l.strip().split(' ')
-        X.append(vec[0])
-        Y.append(vec[1:])
-    fin.close()
+    fin = pd.read_csv(filename)
+    cols=fin.columns
+    X = fin[cols[0]]
+    Y = fin[cols[1]]
+   
     return X, Y