README.md

---
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 ]**

## **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.

---