---
license: creativeml-openrail-m
datasets:
- prithivMLmods/Spam-Text-Detect-Analysis
language:
- en
base_model:
- google-bert/bert-base-uncased
pipeline_tag: text-classification
library_name: transformers
---
### **SPAM DETECTION UNCASED [ SPAM / HAM ]**

This implementation leverages **BERT (Bidirectional Encoder Representations from Transformers)** for binary classification (Spam / Ham) using sequence classification. The model uses the **`prithivMLmods/Spam-Text-Detect-Analysis` dataset** and integrates **Weights & Biases (wandb)** for comprehensive experiment tracking.

---

## **🛠️ Overview**

### **Core Details:**
- **Model:** BERT for sequence classification  
  Pre-trained Model: `bert-base-uncased`
- **Task:** Spam detection - Binary classification task (Spam vs Ham).
- **Metrics Tracked:**  
  - Accuracy  
  - Precision  
  - Recall  
  - F1 Score  
  - Evaluation loss

---

## **📊 Key Results**
Results were obtained using BERT and the provided training dataset:

- **Validation Accuracy:** **0.9937**  
- **Precision:** **0.9931**  
- **Recall:** **0.9597**  
- **F1 Score:** **0.9761**

---

## **📈 Model Training Details**

### **Model Architecture:**
The model uses `bert-base-uncased` as the pre-trained backbone and is fine-tuned for the sequence classification task.

### **Training Parameters:**
- **Learning Rate:** 2e-5  
- **Batch Size:** 16  
- **Epochs:** 3  
- **Loss:** Cross-Entropy

---

## **🚀 How to Train the Model**

1. **Clone Repository:**
   ```bash
   git clone <repository-url>
   cd <project-directory>
   ```

2. **Install Dependencies:**
   Install all necessary dependencies.
   ```bash
   pip install -r requirements.txt
   ```
   or manually:
   ```bash
   pip install transformers datasets wandb scikit-learn
   ```

3. **Train the Model:**
   Assuming you have a script like `train.py`, run:
   ```python
   from train import main
   ```

---

## **✨ Weights & Biases Integration**

### Why Use wandb?
- **Monitor experiments in real time** via visualization.
- Log metrics such as loss, accuracy, precision, recall, and F1 score.
- Provides a history of past runs and their comparisons.

### Initialize Weights & Biases
Include this snippet in your training script:
```python
import wandb
wandb.init(project="spam-detection")
```

---

## 📁 **Directory Structure**

The directory is organized to ensure scalability and clear separation of components:

```
project-directory/
│
├── data/                # Dataset processing scripts
├── wandb/              # Logged artifacts from wandb runs
├── results/            # Save training and evaluation results
├── model/              # Trained model checkpoints
├── requirements.txt    # List of dependencies
└── train.py            # Main script for training the model
```

---

## 🔗 Dataset Information
The training dataset comes from **Spam-Text-Detect-Analysis** available on Hugging Face:
- **Dataset Link:** [Spam Text Detection Dataset - Hugging Face](https://huggingface.co/datasets)

Dataset size:
- **5.57k entries**

---

Let me know if you need assistance setting up the training pipeline, optimizing metrics, visualizing with wandb, or deploying this fine-tuned model. 🚀