Update README.md

README.md

@@ -12,4 +12,41 @@ The original labels include 'true', 'mostly-true', 'half-true', 'barely-true', '
 In this custom mapping, statements labeled as 'true', 'mostly-true', and 'half-true' are all categorized as 'true', while 'barely-true', 'false', and 'pants-fire' are grouped under the 'false' category. 
 This mapping simplifies the classification task into a binary problem, aiming to distinguish between truthful and non-truthful statements.
 
-Bias: The model may inherit biases present in the training data, and it's important to be aware of potential biases in the predictions.
+Bias: The model may inherit biases present in the training data, and it's important to be aware of potential biases in the predictions.
+
+## Code Implementation
+```python
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Load the pre-trained model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(
+    "Arjun24420/BERT-FakeNews-BinaryClassification")
+model = AutoModelForSequenceClassification.from_pretrained(
+    "Arjun24420/BERT-FakeNews-BinaryClassification")
+
+
+def predict(text):
+    # Tokenize the input text and move tensors to the GPU if available
+    inputs = tokenizer(text, padding=True, truncation=True,
+                       max_length=512, return_tensors="pt")
+
+    # Get model output (logits)
+    outputs = model(**inputs)
+
+    probs = outputs.logits.softmax(1)
+    # Get the probabilities for each class
+    class_probabilities = {class_mapping[i]: probs[0, i].item()
+                           for i in range(probs.shape[1])}
+
+    return class_probabilities
+
+
+# Define class labels mapping
+class_mapping = {
+    0: 'reliable',
+    1: 'unreliable',
+}
+
+
+```