readme: ---
  license: apache-2.0
  ---
  
  ### DISTILBERT RUNNING ON [DEEPSPARSE](https://github.com/neuralmagic/deepsparse) GOES BRHMMMMMMMM. 🚀🚀🚀
  
  This model is 👇
  
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          ██╔════╝ ██╔══██╗ ██╔══██╗ ██╔══██╗  ██╔════╝ ██╔════╝
          ███████╗ ██████╔╝ ███████║ ██████╔╝  ███████╗ █████╗  
          ╚════██║ ██╔═══╝  ██╔══██║ ██╔══██╗ ╚════██║█ █╔══╝  
          ███████║ ██║      ██║  ██║ ██║  ██ ║███████║ ███████╗
          ╚══════╝ ╚═╝      ╚═╝  ╚═╝ ╚═╝  ╚═ ╝╚══════╝ ╚══════╝
                                                                                                           
  ![Alt Text](https://media.giphy.com/media/4Hmjz2sqdtASJ2gFMH/giphy.gif)
  
  
  ### LOOKS LIKE THIS 👇
  
  ![Imgur](https://imgur.com/gWfX811.jpg)
  
  ### Inference endpoints, outside of outliers (4ms) is avg. latency on 2 vCPUs:
  
  ![Imgur](https://i.imgur.com/qceSdjZ.png)
  
  
  ### Handler for access to inference endpoints
  
  ```python
  class EndpointHandler:
  
      def __init__(self, path=""):
  
          self.pipeline = Pipeline.create(task="text-classification", model_path=path)
  
      def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
          """
          Args:
              data (:obj:): prediction input text
          """
          inputs = data.pop("inputs", data)
  
          start = perf_counter()
          prediction = self.pipeline(inputs)
          end = perf_counter()
          latency = end - start
  
          return {
              "labels": prediction.labels, 
              "scores": prediction.scores,
              "latency (secs.)": latency
          }
  ```
        
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