john-osborne-j/quantized-clinicalbert
This repository contains a 4-bit quantized ClinicalBERT model for disease classification based on clinical text. Inspired by CheXNet, this model can predict diseases from patient symptom descriptions, particularly focusing on chest-related conditions.
Implements 4-bit quantization via BitsAndBytes to reduce model footprint while maintaining inference performance, enabling deployment on memory-constrained environments. Trained on synthetic clinical text datasets to avoid privacy constraints, with a Flask web interface for direct symptom-to-disease prediction. Integrates with Hugging Face Transformers and supports Google Colab deployment for accessible prototyping.
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Python
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MIT
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Apr 04, 2025
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