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README.md

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----
-license: mit
----
+---
+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