README.md

metadata

language:
  - en
license: apache-2.0
tags:
  - token-classfication
  - int8
  - Intel® Neural Compressor
  - PostTrainingStatic
datasets:
  - conll2003
metrics:
  - accuracy
model-index:
  - name: distilbert-base-uncased-finetuned-conll03-english-int8-static
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: Conll2003
          type: conll2003
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9858650364082395

INT8 distilbert-base-uncased-finetuned-conll03-english

Post-training static quantization

This is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.

The original fp32 model comes from the fine-tuned model elastic/distilbert-base-uncased-finetuned-conll03-english.

The calibration dataloader is the train dataloader. The default calibration sampling size 100 isn't divisible exactly by batch size 8, so the real sampling size is 104.

Test result

	INT8	FP32
Accuracy (eval-accuracy)	0.9859	0.9882
Model size (MB)	64.5	253

Load with optimum:

from optimum.intel import INCModelForTokenClassification

model_id = "Intel/distilbert-base-uncased-finetuned-conll03-english-int8-static"
int8_model = INCModelForTokenClassification.from_pretrained(model_id)