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Dynamically quantized DistilBERT base uncased finetuned SST-2

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Model Details

Model Description: This model is a DistilBERT fine-tuned on SST-2 dynamically quantized with optimum-intel through the usage of huggingface/optimum-intel through the usage of Intel® Neural Compressor.

  • Model Type: Text Classification
  • Language(s): English
  • License: Apache-2.0
  • Parent Model: For more details on the original model, we encourage users to check out this model card.

How to Get Started With the Model


To load the quantized model, you can do as follows:

from optimum.intel.neural_compressor.quantization import IncQuantizedModelForSequenceClassification

model = IncQuantizedModelForSequenceClassification.from_pretrained("Intel/distilbert-base-uncased-finetuned-sst-2-english-int8-dynamic")


This is an INT8 ONNX model quantized with Intel® Neural Compressor.

The original fp32 model comes from the fine-tuned model DistilBERT.

Test result

Accuracy (eval-accuracy) 0.9037 0.9106
Model size (MB) 73 256

Load ONNX model:

from optimum.onnxruntime import ORTModelForSequenceClassification
model = ORTModelForSequenceClassification.from_pretrained('Intel/distilbert-base-uncased-finetuned-sst-2-english-int8-dynamic')
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Hosted inference API
Text Classification
This model can be loaded on the Inference API on-demand.

Datasets used to train Intel/distilbert-base-uncased-finetuned-sst-2-english-int8-dynamic