readme: ---
  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](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor). 
  
  The original fp32 model comes from the fine-tuned model [elastic/distilbert-base-uncased-finetuned-conll03-english](https://huggingface.co/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:
  
  ```python
  from optimum.intel.neural_compressor.quantization import IncQuantizedModelForTokenClassification
  int8_model = IncQuantizedModelForTokenClassification.from_pretrained(
      'Intel/distilbert-base-uncased-finetuned-conll03-english-int8-static',
  )
  ```
  