cryptofyre-ai/roberta-spam-onnx

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.

# For CPU
pip install onnxruntime transformers

# OR for NVIDIA GPU
pip install onnxruntime-gpu transformers