| 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._ |