Update README.md

README.md

@@ -13,11 +13,6 @@ license: apache-2.0
 ```python
 from sentence_transformers import CrossEncoder
 import numpy as np
-
-# Function that applies sigmoid to a score
-def sigmoid(x):
-    return 1 / (1 + np.exp(-x))
-
 model = CrossEncoder('HeTree/HeCross')
 
 # Scores (already after sigmoid)
@@ -31,6 +26,11 @@ You can use the model also directly with Transformers library (without SentenceT
 ```python
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
+
+# Function that applies sigmoid to a score
+def sigmoid(x):
+    return 1 / (1 + np.exp(-x))
+
 model = AutoModelForSequenceClassification.from_pretrained('HeTree/HeCross')
 tokenizer = AutoTokenizer.from_pretrained('HeTree/HeCross')
 features = tokenizer(['כמה אנשים חיים בברלין?', 'כמה אנשים חיים בברלין?'],