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---
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. 🚀 |