readme: ---
  tags:
  - text-classification
  - endpoints-template
  - optimum
  library_name: generic
  ---
  
  # Optimized and Quantized DistilBERT with a custom pipeline with handler.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 `handler.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._