Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- Pulk17/Fake-News-Detection-dataset
+language:
+- en
+metrics:
+- accuracy
+- precision
+- recall
+base_model:
+- google-bert/bert-base-uncased
+pipeline_tag: text-classification
+library_name: transformers
+---
+
+# Fake News Detection Model
+
+## Model Overview
+
+This model is designed to classify news articles as real or fake based on their textual content. It uses a BERT-based transformer model (`bert-base-uncased`) fine-tuned on a custom dataset of news articles. The model predicts whether a given article is fake or real with high accuracy.
+
+## Model License
+
+This model is licensed under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).
+
+## Datasets Used
+
+The model was trained on a variety of datasets, including:
+- **Fake News Dataset**: Contains labeled news articles with "fake" or "real" classifications.
+- **News Articles Dataset**: A collection of news articles used for training and validation.
+
+## Languages
+
+The model primarily works with English-language news articles, but it could be extended to other languages with appropriate data.
+
+## Metrics
+
+The model's performance was evaluated on the following metrics:
+- **Accuracy**: 99.58%
+- **Precision**: 99.27%
+- **Recall**: 99.88%
+- **ROC-AUC**: 99.99%
+- **F1-Score**: 99.57%
+
+## Model Details
+
+- **Base Model**: `bert-base-uncased`
+- **Fine-Tuning**: The model was fine-tuned on a news dataset with labeled examples of real and fake news.
+- **Training Epochs**: 3
+- **Batch Size**: 32
+- **Optimizer**: Adam with weight decay
+- **Learning Rate**: 2e-5
+
+## Usage
+
+To use this model, you can interact with it via the Hugging Face Inference API or integrate it into your Python-based applications.
+
+Example code for inference:
+```python
+import requests
+
+url = "https://api-inference.huggingface.co/models/your-username/fake-news-bert"
+headers = {"Authorization": "Bearer YOUR_HUGGINGFACE_API_KEY"}
+payload = {"inputs": "The news article content here"}
+
+response = requests.post(url, headers=headers, json=payload)
+prediction = response.json()
+
+print(f"Prediction: {prediction}")