ONNX Unleashed: Training and Optimizing BERT Models for Streamlit Web Apps

Author(s): Marcello Politi

Originally published on Towards AI.

Learn to quantize and deploy your Deep Learning model with ONNX
Photo by 愚木混株 cdd20 on Unsplash

In this article, I want to accomplish something very simple: build a web app that recognizes an emotion given a sentence. In doing this though we will see how to train a transformer-based model, convert it to ONNX format, quantize it, and run it from the frontend using Streamlit.

You can tun the following scripts using Deepnote: a cloud-based notebook that’s great for collaborative data science projects, and good for prototyping.

Optimizing the model with techniques such as quantization may be a good idea if we can maintain good performance, as it will improve the response speed, and we can create a product with lower latency and ensure greater user satisfaction.

We use a BERT-based model for emotion detection: anger, fear, joy, love, sadness, and surprise.

This is a model released by Microsoft, which is a distilled version of BERT.

model: https://huggingface.co/microsoft/xtremedistil-l6-h256-uncaseddataset: https://huggingface.co/datasets/dair-ai/emotion

We will heavily use the hugging face APIs to train this model on this dataset.

Let’s start by installing the needed libraries. We are going to use a lot of the transformers and ONNX ones.

!pip install transformers[torch]!pip install datasets onnx onnxruntime !pip install accelerate -U

All the imports we need:

from datasets import load_datasetfrom transformers import AutoTokenizerimport torchfrom transformers import AutoModelForSequenceClassificationimport… Read the full blog for free on Medium.

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Published via Towards AI