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---
language:
- en
license: apache-2.0
tags:
- text-classfication
- int8
- 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 [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
- Batch size = 8
- [Amazon Web Services](https://aws.amazon.com/) c6i.xlarge (Intel ICE Lake: 4 vCPUs, 8g Memory) instance.
| |INT8|FP32|
|---|:---:|:---:|
| **Throughput (samples/sec)** |148.144|77.108|
| **Accuracy (eval-f1)** |0.9859|0.9882|
| **Model size (MB)** |64.5|253|
### Load with Intel® Neural Compressor (build from source):
```python
from neural_compressor.utils.load_huggingface import OptimizedModel
int8_model = OptimizedModel.from_pretrained(
'Intel/distilbert-base-uncased-finetuned-conll03-english-int8-static',
)
```
Notes:
- The INT8 model has better performance than the FP32 model when the CPU is fully occupied. Otherwise, there will be the illusion that INT8 is inferior to FP32.
|