Fine-Tuning Transformer Models for IoT Sensor Data Analysis

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Hi all, I’m currently working on fine-tuning transformer models to process and predict patterns from IoT sensor data, specifically multivariate time-series streams. The IoT framework I’m using follows a typical setup. While researching general IoT architecture, I came across this helpful article https://www.theengineeringprojects.com/2023/06/top-iot-starter-kits-for-the-beginners-to-learn-programming.html which outlines the system-level design I’m implementing. Still, I need more guidance on applying transformer models for this specific use case.

Has anyone here worked with fine-tuning Hugging Face models on IoT sensor data before? Any tips for optimizing training with limited hardware would be appreciated.

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I’m not very familiar with it, but I think this is the class that comes closest to what you’re looking for in Transoformers…

When deploying to IoT, I think most people convert to ONNX format and then to TensorRT format if necessary.