Update README.md

README.md

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-{}
+license: apache-2.0
+tags:
+- text-classification
+- fake-news-detection
+---
+
+# Fake News Detection Model
+
+This model is trained to detect fake news articles using DistilBERT.
+
+## Training Data
+
+The model was trained on a dataset of fake and real news articles. The dataset was preprocessed to remove irrelevant information and to balance the classes.
+
+## Performance
+
+The model was evaluated using 5-fold cross-validation. The average metrics across all folds are as follows:
+
+| Metric    | Value |
+|-----------|-------|
+| Accuracy  | 0.973 |
+| Precision | 0.962 |
+| Recall    | 0.986 |
+| F1        | 0.973 |
+| ROC AUC   | 0.973 |
+
+## Usage 
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("HugMi/M3-Assignment2")
+model = AutoModelForSequenceClassification.from_pretrained("HugMi/M3-Assignment2")
+
+def classify_text(text):
+    inputs = tokenizer(text, return_tensors="pt")
+    outputs = model(**inputs)
+    predicted_class = outputs.logits.argmax().item()
+    return predicted_class  # 0 for fake, 1 for real
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