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README.md

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+# 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:** [email protected]
+- **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).*
+