Update README.md

README.md

@@ -1,3 +1,45 @@
----
-license: mit
----
+---
+license: mit
+language: en
+tags:
+  - text-classification
+  - fake-news-detection
+  - transformer-ensemble
+  - bert
+  - deberta
+library_name: transformers
+datasets:
+  - custom
+model_name: BertAndDeberta
+---
+
+# BertAndDeberta — Transformer Ensemble for Fake News Detection
+
+This repository hosts a fine-tuned transformer ensemble model that combines **BERT** and **DeBERTa** for binary **fake news classification**. The model was trained on a custom-labeled dataset containing real and fake news articles sourced from Kaggle and Hugging Face datasets.
+
+## Model Details
+
+- **Architecture**: Ensemble of BERT-base and DeBERTa-base
+- **Task**: Binary Text Classification (`REAL` vs `FAKE`)
+- **Training Framework**: PyTorch using 🤗 Transformers
+- **License**: MIT
+
+## Dataset
+
+The dataset is a custom collection combining:
+- News content (title + body)
+- Labels: `0 = FAKE`, `1 = REAL`
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("fauxNeuz/BertAndDeberta")
+model = AutoModelForSequenceClassification.from_pretrained("fauxNeuz/BertAndDeberta")
+
+text = "Government confirms policy updates in healthcare sector."
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+pred = outputs.logits.argmax(dim=-1).item()
+print("REAL" if pred == 1 else "FAKE")