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模型:

philschmid/distilbert-onnx-banking77

任务:

文本分类

类库:

ONNX

其他:

generic endpoints-template optimum
模型介绍 文件清单
中文

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:

  • Specify the requirements by defining a requirements.txt file.
  • 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.

    作者:

    Philipp Schmid

    数据集大小:

    165.45 MB

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