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Optimized and Quantized DistilBERT with a custom pipeline.py

NOTE: Blog post coming soon

This is a template repository for Text Classification using Optimum and onnxruntime to support generic inference with Hugging Face Hub generic Inference API. There are two required steps:

  1. Specify the requirements by defining a requirements.txt file.
  2. Implement the pipeline.py __init__ and __call__ methods. These methods are called by the Inference API. The __init__ method should load the model and preload the optimum model and tokenizers as well as the text-classification pipeline needed for inference. This is only called once. The __call__ method performs the actual inference. Make sure to follow the same input/output specifications defined in the template for the pipeline to work.

add

library_name: generic

to the readme.

note: the generic community image currently only support inputs as parameter and no parameter.

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