Create README.md

README.md

@@ -0,0 +1,31 @@
+## Model Details
+This model is a Binart classification model fine-tuned on the FakeOrRealNews Dataset using the BERT (bert-base-uncased) architecture. The primary task is to classify news articles into different categories, making it suitable for fake news detection. BERT (Bidirectional Encoder Representations from Transformers) is a transformer-based model known for its effectiveness in natural language processing tasks.
+
+It takes the title of the news article and classifies it into Reliable or Unreliable news.
+
+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
+
+tokenizer = AutoTokenizer.from_pretrained("Arjun24420/BERT-FakeOrReal-BinaryClassification")
+model = AutoModelForSequenceClassification.from_pretrained("Arjun24420/BERT-FakeOrReal-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
+
+```