Update README.md

README.md

@@ -18,6 +18,57 @@ To model this threshold effectively, we opted for the tanh activation function,
 
 Utilizing this approach, we demonstrated improvements in regression tasks for evaluating stances on each test scenario. While the overall MSE did not show significant improvement, we observed higher accuracy, recall, and precision for the regression tasks. It is important to note that the classification task specified in Qiu et al. (2022) solely determines the presence or absence of the value in question, without considering the specific stance presented in the text. Therefore, our regression task, which assesses the particular stance, should not be directly compared with the classification task from Qiu et al. (2022).
 
+## 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.")
+
+
+
+
 
 ## Results from Qiu et al. (2022)