Update README.md

README.md

@@ -8,4 +8,146 @@ base_model:
 - google-bert/bert-base-uncased
 pipeline_tag: text-classification
 library_name: transformers
+---
+Below is a `README.md` template that you can use to document the project with appropriate details:
+
+---
+
+# 📊 **Spam Detection with BERT using Hugging Face & Weights & Biases**
+
+## **Overview**
+
+This project implements a spam detection model using the **BERT (Bidirectional Encoder Representations from Transformers)** architecture and leverages **Weights & Biases (wandb)** for experiment tracking. The model is trained and evaluated using the [prithivMLmods/Spam-Text-Detect-Analysis](https://huggingface.co/datasets/prithivMLmods/Spam-Text-Detect-Analysis) dataset from Hugging Face.
+
+---
+
+## **🛠️ Requirements**
+
+- Python 3.x
+- PyTorch
+- Transformers
+- Datasets
+- Weights & Biases
+- Scikit-learn
+
+---
+
+### **Install Dependencies**
+
+You can install the required dependencies with the following:
+
+```bash
+pip install transformers datasets wandb scikit-learn
+```
+
+---
+
+## **📈 Model Training**
+
+### **Model Architecture**
+The model uses **BERT for sequence classification**:
+- Pre-trained Model: `bert-base-uncased`
+- Task: Binary classification (Spam / Ham)
+- Optimization: Cross-entropy loss
+
+---
+
+### **Training Arguments**
+- **Learning rate:** `2e-5`
+- **Batch size:** 16
+- **Epochs:** 3
+- **Evaluation:** Epoch-based.
+
+---
+
+## **🔗 Dataset**
+
+The model uses the **Spam Text Detection Dataset** available at [Hugging Face Datasets](https://huggingface.co/datasets/prithivMLmods/Spam-Text-Detect-Analysis).
+
+You can access the dataset [here](https://huggingface.co/datasets/prithivMLmods/Spam-Text-Detect-Analysis).
+
+---
+
+## **🖥️ Instructions**
+
+### Clone and Set Up
+Clone the repository, if applicable:
+
+```bash
+git clone <repository-url>
+cd <project-directory>
+```
+
+Ensure dependencies are installed with:
+
+```bash
+pip install -r requirements.txt
+```
+
+---
+
+### Train the Model
+After installing dependencies, you can train the model using:
+
+```python
+from train import main  # Assuming training is implemented in a `train.py`
+```
+
+Replace `train.py` with your script's entry point.
+
+---
+
+## **✨ Weights & Biases Integration**
+
+We use **Weights & Biases** for:
+- Real-time logging of training and evaluation metrics.
+- Tracking experiments.
+- Monitoring evaluation loss, precision, recall, and accuracy.
+
+Set up wandb by initializing this in the script:
+
+```python
+import wandb
+wandb.init(project="spam-detection")
+```
+
+---
+
+## **📊 Metrics**
+
+The following metrics were logged:
+
+- **Accuracy:** Final validation accuracy.
+- **Precision:** Fraction of predicted positive cases that were truly positive.
+- **Recall:** Fraction of actual positive cases predicted.
+- **F1 Score:** Harmonic mean of precision and recall.
+- **Evaluation Loss:** Loss during validation on evaluation splits.
+
+---
+
+## **🚀 Results**
+
+Using BERT with the provided dataset:
+
+- **Validation Accuracy:** `0.9937`
+- **Precision:** `0.9931`
+- **Recall:** `0.9597`
+- **F1 Score:** `0.9761`
+
+---
+
+## **📁 Files and Directories**
+
+- `model/`: Contains trained model checkpoints.
+- `data/`: Scripts for processing datasets.
+- `wandb/`: All logged artifacts from Weights & Biases runs.
+- `results/`: Training and evaluation results are saved here.
+
+---
+
+## **📜 Acknowledgements**
+
+Dataset Source: [Spam-Text-Detect-Analysis on Hugging Face](https://huggingface.co/datasets/prithivMLmods/Spam-Text-Detect-Analysis)  
+Model: **BERT for sequence classification** from Hugging Face Transformers.
+
 ---