
# 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._
{"library_name": "generic", "tags": ["text-classification", "endpoints-template", "optimum"]}
generic text-classification endpoints-template optimum endpoints_compatible region:us
TAGS
#generic #text-classification #endpoints-template #optimum #endpoints_compatible #region-us 


# Optimized and Quantized DistilBERT with a custom pipeline with URL

> 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 'URL' file.
2. Implement the 'URL' '__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 

to the readme.

_note: the 'generic' community image currently only support 'inputs' as parameter and no parameter._
# Optimized and Quantized DistilBERT with a custom pipeline with URL

> 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 'URL' file.
2. Implement the 'URL' '__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 

to the readme.

_note: the 'generic' community image currently only support 'inputs' as parameter and no parameter._
TAGS
#generic #text-classification #endpoints-template #optimum #endpoints_compatible #region-us 
 # Optimized and Quantized DistilBERT with a custom pipeline with URL

> 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 'URL' file.
2. Implement the 'URL' '__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 

to the readme.

_note: the 'generic' community image currently only support 'inputs' as parameter and no parameter._
TAGS
#generic #text-classification #endpoints-template #optimum #endpoints_compatible #region-us 
# Optimized and Quantized DistilBERT with a custom pipeline with URL

> 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 'URL' file.
2. Implement the 'URL' '__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 

to the readme.

_note: the 'generic' community image currently only support 'inputs' as parameter and no parameter._