pszemraj/gibberish_detector_onnx-quant-avx2

gibberish_detector_onnx: quantized avx2

# pip install 'optimum[onnxruntime,exporters]'
from optimum.pipelines import pipeline


classifier = pipeline(
    "text-classification",
    model="pszemraj/gibberish_detector_onnx-quant-avx2",
    accelerator="ort",
)
classifier("ayy waddup")
# [{'label': 'noise', 'score': 0.38642483949661255}]

differences between quant params

the one with -pc suffix means per_channel=True

>>> src = 'quant_onnx_gibberish_detector' # avx2
>>> classifier = pipeline('text-classification', model=src, accelerator='ort')
>>> classifier('ayy waddup')
[{'label': 'noise', 'score': 0.34829846024513245}]
>>> src = 'quant_onnx_gibberish_detector-pc' # avx2 per channel (this model)
>>> classifier = pipeline('text-classification', model=src, accelerator='ort')
>>> classifier('ayy waddup')
[{'label': 'noise', 'score': 0.38642483949661255}]
>>> src = 'onnx_gibberish_detector' # unquantized onnx
>>> classifier = pipeline('text-classification', model=src, accelerator='ort')
>>> classifier('ayy waddup')
[{'label': 'noise', 'score': 0.6847617626190186}]