Replace pipeline.py with handler.py

README.md

@@ -6,14 +6,14 @@ tags:
 library_name: generic
 ---
 
-# Optimized and Quantized DistilBERT with a custom pipeline.py
+# 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 `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.
+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 
 ```