Update README.md

README.md

@@ -20,51 +20,51 @@ 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.")
+  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.")