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---
license: apache-2.0
---
### DISTILBERT RUNNING ON [DEEPSPARSE](https://github.com/neuralmagic/deepsparse) GOES BRHMMMMMMMM. πŸš€πŸš€πŸš€
This model is πŸ‘‡
β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•— β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•— β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•— β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•— β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•— β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•—
β–ˆβ–ˆβ•”β•β•β•β•β• β–ˆβ–ˆβ•”β•β•β–ˆβ–ˆβ•— β–ˆβ–ˆβ•”β•β•β–ˆβ–ˆβ•— β–ˆβ–ˆβ•”β•β•β–ˆβ–ˆβ•— β–ˆβ–ˆβ•”β•β•β•β•β• β–ˆβ–ˆβ•”β•β•β•β•β•
β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•— β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•”β• β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•‘ β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•”β• β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•— β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•—
β•šβ•β•β•β•β–ˆβ–ˆβ•‘ β–ˆβ–ˆβ•”β•β•β•β• β–ˆβ–ˆβ•”β•β•β–ˆβ–ˆβ•‘ β–ˆβ–ˆβ•”β•β•β–ˆβ–ˆβ•— β•šβ•β•β•β•β–ˆβ–ˆβ•‘β–ˆ β–ˆβ•”β•β•β•
β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•‘ β–ˆβ–ˆβ•‘ β–ˆβ–ˆβ•‘ β–ˆβ–ˆβ•‘ β–ˆβ–ˆβ•‘ β–ˆβ–ˆ β•‘β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•‘ β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•—
β•šβ•β•β•β•β•β•β• β•šβ•β• β•šβ•β• β•šβ•β• β•šβ•β• β•šβ• β•β•šβ•β•β•β•β•β•β• β•šβ•β•β•β•β•β•β•
![Alt Text](https://media.giphy.com/media/4Hmjz2sqdtASJ2gFMH/giphy.gif)
### LOOKS LIKE THIS πŸ‘‡
![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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