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--- |
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tags: |
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- text-classification |
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- endpoints-template |
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- optimum |
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library_name: generic |
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--- |
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# Optimized and Quantized DistilBERT with a custom pipeline.py |
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> NOTE: Blog post coming soon |
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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: |
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1. Specify the requirements by defining a `requirements.txt` file. |
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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. |
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add |
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``` |
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library_name: generic |
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``` |
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to the readme. |
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_note: the `generic` community image currently only support `inputs` as parameter and no parameter._ |
