---
license: apache-2.0
language:
- en
base_model:
- google-bert/bert-base-uncased
---
# Spam Detector BERT MoE v2.2

[![Hugging Face](https://img.shields.io/badge/Hugging%20Face-Model-blue)](https://huggingface.co./AntiSpamInstitute/spam-detector-bert-MoE-v2.2)
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE)

## Table of Contents
- [Overview](#overview)
- [Model Description](#model-description)
- [Features](#features)
- [Usage](#usage)
  - [Installation](#installation)
  - [Quick Start](#quick-start)
  - [Example](#example)
- [Model Architecture](#model-architecture)
- [Training Data](#training-data)
- [Performance](#performance)
- [Intended Use](#intended-use)
- [Limitations](#limitations)
- [Citing This Model](#citing-this-model)
- [License](#license)
- [Contact](#contact)

## Overview

The **Spam Detector BERT MoE v2.2** is a state-of-the-art natural language processing model designed to accurately classify text messages and content as spam or non-spam. Leveraging a BERT-based architecture enhanced with a Mixture of Experts (MoE) approach, this model achieves high performance and scalability for diverse spam detection applications.

## Model Description

This model is built upon the BERT (Bidirectional Encoder Representations from Transformers) architecture and incorporates a Mixture of Experts (MoE) mechanism to improve its ability to handle a wide variety of spam patterns. The MoE layer allows the model to activate different "experts" (sub-models) based on the input, enhancing its capacity to generalize across different types of spam content.

- **Model Name:** spam-detector-bert-MoE-v2.2
- **Architecture:** BERT with Mixture of Experts (MoE)
- **Language:** English
- **Task:** Text Classification (Spam Detection)

## Features

- **High Accuracy:** Achieves superior performance in distinguishing spam from non-spam messages.
- **Scalable:** Efficiently handles large datasets and real-time classification tasks.
- **Versatile:** Suitable for various applications, including email filtering, SMS spam detection, and social media monitoring.
- **Pre-trained:** Ready-to-use with extensive pre-training on diverse datasets.

## Usage

### Installation

First, ensure you have the [Transformers](https://github.com/huggingface/transformers) library installed. You can install it via pip:

```bash
pip install transformers
```

### Quick Start

Here's how to quickly get started with the **Spam Detector BERT MoE v2.2** model using the Hugging Face `transformers` library:

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("AntiSpamInstitute/spam-detector-bert-MoE-v2.2")
model = AutoModelForSequenceClassification.from_pretrained("AntiSpamInstitute/spam-detector-bert-MoE-v2.2")

# Sample text
texts = [
    "Congratulations! You've won a $1,000 Walmart gift card. Click here to claim now.",
    "Hey, are we still meeting for lunch today?"
]

# Tokenize the input
inputs = tokenizer(texts, return_tensors="pt", padding=True, truncation=True)

# Get model predictions
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits

# Apply softmax to get probabilities
probabilities = torch.softmax(logits, dim=1)

# Get predicted labels
predictions = torch.argmax(probabilities, dim=1)

# Map labels to class names
label_map = {0: "Not Spam", 1: "Spam"}
for text, prediction in zip(texts, predictions):
    print(f"Text: {text}\nPrediction: {label_map[prediction.item()]}\n")
```

### Example

**Input:**
```plaintext
"Limited time offer! Buy one get one free on all products. Visit our store now!"
```

**Output:**
```plaintext
Prediction: Spam
```

## Model Architecture

The **Spam Detector BERT MoE v2.2** employs the following architecture components:

- **BERT Base:** Utilizes the pre-trained BERT base model with 12 transformer layers, 768 hidden units, and 12 attention heads.
- **Mixture of Experts (MoE):** Incorporates an MoE layer that consists of multiple expert feed-forward networks. During inference, only a subset of experts are activated based on the input, enhancing the model's capacity without significantly increasing computational costs.
- **Classification Head:** A linear layer on top of the BERT embeddings for binary classification (spam vs. not spam).

## Training Data

The model was trained on a diverse and extensive dataset comprising:

- **Public Spam Datasets:** Including SMS Spam Collection, Enron Email Dataset, and various social media spam datasets.
- **Synthetic Data:** Generated to augment the training set and cover a wide range of spam scenarios.
- **Real-World Data:** Collected from multiple domains to ensure robustness and generalization.

The training data was preprocessed to remove personally identifiable information (PII) and ensure compliance with data privacy standards.

## Performance

The **Spam Detector BERT MoE v2.2** achieves the following performance metrics on benchmark datasets:

| Dataset               | Accuracy | Precision | Recall | F1 Score |
|-----------------------|----------|-----------|--------|----------|
| SMS Spam Collection   | 98.5%    | 98.7%     | 98.3%  | 98.5%    |
| Enron Email Dataset   | 97.8%    | 98.0%     | 97.5%  | 97.7%    |
| Social Media Spam     | 96.5%    | 96.7%     | 96.3%  | 96.5%    |

*Note: These metrics are based on the model's performance at the time of release and may vary with different data distributions.*

## Intended Use

The **Spam Detector BERT MoE v2.2** is intended for use in the following applications:

- **Email Filtering:** Automatically classify and filter spam emails.
- **SMS Spam Detection:** Identify and block spam messages in mobile communications.
- **Social Media Monitoring:** Detect and manage spam content on platforms like Twitter and Facebook.
- **Content Moderation:** Assist in maintaining the quality of user-generated content by filtering out unwanted spam.

## Limitations

While the **Spam Detector BERT MoE v2.2** demonstrates high accuracy, users should be aware of the following limitations:

- **Language Support:** Currently optimized for English text. Performance may degrade for other languages.
- **Evolving Spam Tactics:** Spammers continually adapt their strategies, which may affect the model's effectiveness over time. Regular updates and retraining are recommended.
- **Context Understanding:** The model primarily focuses on textual features and may not fully capture contextual nuances or intent beyond the text.
- **Resource Requirements:** The MoE architecture, while efficient, may require substantial computational resources for deployment in resource-constrained environments.

## Citing This Model

If you use the **Spam Detector BERT MoE v2.2** in your research or applications, please cite it as follows:

```bibtex
@misc{AntiSpamInstitute_spam-detector-bert-MoE-v2.2,
  author       = {AntiSpamInstitute},
  title        = {spam-detector-bert-MoE-v2.2},
  year         = {2024},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co./AntiSpamInstitute/spam-detector-bert-MoE-v2.2}
}
```

## License

This model is released under the [Apache 2.0 License](LICENSE). Please review the license terms before using the model.

## Contact

For questions, issues, or contributions, please reach out:

- **GitHub Repository:** [AntiSpamInstitute/spam-detector-bert-MoE-v2.2](https://github.com/AntiSpamInstitute/spam-detector-bert-MoE-v2.2)
- **Email:** contact@antispaminstitute.org
- **Twitter:** [@AntiSpamInstitute](https://twitter.com/AntiSpamInstitute)

---

*This README was generated to provide comprehensive information about the Spam Detector BERT MoE v2.2 model. For the latest updates and more detailed documentation, please visit the [Hugging Face model page](https://huggingface.co./AntiSpamInstitute/spam-detector-bert-MoE-v2.2).*