README.md

---
license: mit
language: en
library_name: onnxruntime
pipeline_tag: text-classification
tags:
- roberta
- spam
- text-classification
- onnx
- distilled
- quantized
base_model: mshenoda/roberta-spam
---

# ONNX Distilled Spam Classifier

This repository contains a distilled and quantized version of a RoBERTa-based spam classification model, optimized for high-performance CPU inference in the ONNX format.

This model was created by distilling `mshenoda/roberta-spam` for the purpose of efficient on-device and cross-platform deployment.

## Model Description

* **Model Type:** A distilled RoBERTa-base model.
* **Task:** Spam classification (binary classification).
* **Format:** ONNX, with dynamic quantization.
* **Key Features:** Lightweight, fast, and ideal for CPU-based inference.

## Intended Uses & Limitations

This model is designed for client-side applications where performance and low resource usage are critical. It's perfect for:

* Desktop applications (Windows, Linux, macOS)
* Mobile applications (with an appropriate ONNX runtime)
* Edge devices

As a distilled model, there may be a minor trade-off in accuracy compared to the larger `roberta-base` teacher model, in exchange for a significant boost in speed and a smaller memory footprint.

## How to Get Started

You can use this model directly with the `onnxruntime` and `transformers` libraries.

### 1. Installation

First, make sure you have the necessary libraries installed. For GPU usage, install `onnxruntime-gpu`; for CPU-only, `onnxruntime` is sufficient.

```bash
# For CPU
pip install onnxruntime transformers

# OR for NVIDIA GPU
pip install onnxruntime-gpu transformers