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zeroshot/sst2-distilbert-sparse

By zeroshot · ↓ 0 · ♥ 0 · Updated 2022-12-19

What's in this repo

This page lists the files in zeroshot/sst2-distilbert-sparse, including model.onnx.

  • 1 ONNX models (.onnx files)
  • 9 files, including 3 .json, 1 .md, 1 .py, 1 .onnx, 1 .txt
  • Last updated 2022-12-19

ONNX files

File Size Download
model.onnx — Download

How to use

These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.

README

DISTILBERT RUNNING ON DEEPSPARSE GOES BRHMMMMMMMM. 🚀🚀🚀

This model is 👇

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    ███████╗ ██████╔╝ ███████║ ██████╔╝  ███████╗ █████╗  
    ╚════██║ ██╔═══╝  ██╔══██║ ██╔══██╗ ╚════██║█ █╔══╝  
    ███████║ ██║      ██║  ██║ ██║  ██ ║███████║ ███████╗
    ╚══════╝ ╚═╝      ╚═╝  ╚═╝ ╚═╝  ╚═ ╝╚══════╝ ╚══════╝

Alt Text

LOOKS LIKE THIS 👇

Imgur

Inference endpoints, outside of outliers (4ms) is avg. latency on 2 vCPUs:

Imgur

Handler for access to inference endpoints

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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Model info

text-classification transformers
transformers onnx distilbert text-classification license:apache-2.0 text-embeddings-inference endpoints_compatible region:us
↓ 0 ♥ 0

Created: 2022-11-10

Updated: 2022-12-19

View on Hugging Face

Files (9)

.gitattributes
.gitignore
README.md
config.json
handler.py
model.onnx ONNX
requirements.txt
tokenizer.json
tokenizer_config.json

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