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