|
--- |
|
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) |
|
``` |
|
|
