prithivMLmods
/

Spam-Bert-Uncased

@@ -11,97 +11,83 @@ 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")
@@ -109,41 +95,30 @@ 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.
----

 ---
 ### **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. 🚀