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Update README.md
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README.md
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---
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license: apache-2.0
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---
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# Quantized BERT-base MNLI model with 90% of usntructured sparsity
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The pruned and quantized model in the OpenVINO IR. The pruned model was taken from this [source](https://huggingface.co/neuralmagic/oBERT-12-downstream-pruned-unstructured-90-mnli) and quantized with the code below using HF Optimum for OpenVINO:
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# Load the default quantization configuration detailing the quantization we wish to apply
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quantization_config = OVConfig()
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# Instantiate our OVQuantizer using the desired configuration
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quantizer = OVQuantizer.from_pretrained(model)
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# Create the calibration dataset used to perform static quantization
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calibration_dataset = quantizer.get_calibration_dataset(
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---
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license: apache-2.0
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datasets:
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- mnli
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metrics:
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- accuracy
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tags:
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- sequence-classification
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- int8
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---
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# Quantized BERT-base MNLI model with 90% of usntructured sparsity
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The pruned and quantized model in the OpenVINO IR. The pruned model was taken from this [source](https://huggingface.co/neuralmagic/oBERT-12-downstream-pruned-unstructured-90-mnli) and quantized with the code below using HF Optimum for OpenVINO:
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# Load the default quantization configuration detailing the quantization we wish to apply
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quantization_config = OVConfig()
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# Instantiate our OVQuantizer using the desired configuration
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quantizer = OVQuantizer.from_pretrained(model, feature="sequence-classification")
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# Create the calibration dataset used to perform static quantization
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calibration_dataset = quantizer.get_calibration_dataset(
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