Update README.md

README.md

@@ -20,54 +20,56 @@ Utilizing this approach, we demonstrated improvements in regression tasks for ev
 
 ## Usage
 
-  import torch
-  from transformers import AutoTokenizer, AutoModelForSequenceClassification
-  
-  # Load the model and tokenizer
-  model_path = 'nharrel/Valuesnet_DeBERTa_v3'
-  tokenizer = AutoTokenizer.from_pretrained(model_path)
-  model = AutoModelForSequenceClassification.from_pretrained(model_path)
-  model.eval()
-
-  # Define maximum length for padding and truncation
-  max_length = 128
-  
-  def custom_round(x):
-      if x >= 0.50:
-          return 1
-      elif x < -0.50:
-          return -1
-      else:
-          return 0
-  
-  def predict(text):
-      inputs = tokenizer(text, padding='max_length', truncation=True, max_length=max_length, return_tensors='pt')
-      with torch.no_grad():
-          outputs = model(**inputs)
-  
-      prediction = torch.tanh(outputs.logits).cpu().numpy()
-      rounded_prediction = custom_round(prediction)
-      return rounded_prediction
-  
-  def test_sentence(sentence):
-      prediction = predict(sentence)
-      label_map = {-1: 'Against', 0: 'Not Present', 1: 'Supports'}
-      predicted_label = label_map.get(prediction, 'unknown')
-      print(f"Sentence: {sentence}")
-      print(f"Predicted Label: {predicted_label}")
-  
-  # Define Schwartz's 10 values
-  schwartz_values = [
-      "BENEVOLENCE", "UNIVERSALISM", "SELF-DIRECTION", "STIMULATION", "HEDONISM",
-      "ACHIEVEMENT", "POWER", "SECURITY", "CONFORMITY", "TRADITION"
-  ]
-  
-  for value in schwartz_values:
-      print("Values stance is: " + value)
-      test_sentence(f"[{value}] You are a very pleasant person to be around.")
-
-
-
+```python
+
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Load the model and tokenizer
+model_path = 'nharrel/Valuesnet_DeBERTa_v3'
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+model.eval()
+
+# Define maximum length for padding and truncation
+max_length = 128
+
+def custom_round(x):
+    if x >= 0.50:
+        return 1
+    elif x < -0.50:
+        return -1
+    else:
+        return 0
+
+def predict(text):
+    inputs = tokenizer(text, padding='max_length', truncation=True, max_length=max_length, return_tensors='pt')
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    prediction = torch.tanh(outputs.logits).cpu().numpy()
+    rounded_prediction = custom_round(prediction)
+    return rounded_prediction
+
+def test_sentence(sentence):
+    prediction = predict(sentence)
+    label_map = {-1: 'Against', 0: 'Not Present', 1: 'Supports'}
+    predicted_label = label_map.get(prediction, 'unknown')
+    print(f"Sentence: {sentence}")
+    print(f"Predicted Label: {predicted_label}")
+
+# Define Schwartz's 10 values
+schwartz_values = [
+    "BENEVOLENCE", "UNIVERSALISM", "SELF-DIRECTION", "STIMULATION", "HEDONISM",
+    "ACHIEVEMENT", "POWER", "SECURITY", "CONFORMITY", "TRADITION"
+]
+
+for value in schwartz_values:
+    print("Values stance is: " + value)
+    test_sentence(f"[{value}] You are a very pleasant person to be around.")
+
+
+```
 
 
 ## Results from Qiu et al. (2022)