
# INT8 DistilBERT base cased finetuned on Squad

### 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 [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad).

The calibration dataloader is the train dataloader. The default calibration sampling size 300 isn't divisible exactly by batch size 8, so the real sampling size is 304.

The linear module **distilbert.transformer.layer.1.ffn.lin2** falls back to fp32 to meet the 1% relative accuracy loss.

### Test result

|   |INT8|FP32|
|---|:---:|:---:|
| **Accuracy (eval-f1)** |86.0005|86.8373|
| **Model size (MB)**  |71.2|249|

### Load with optimum:

```python
from optimum.intel.neural_compressor.quantization import IncQuantizedModelForQuestionAnswering
int8_model = IncQuantizedModelForQuestionAnswering(
    'Intel/distilbert-base-cased-distilled-squad-int8-static',
)
```

{"license": "apache-2.0", "tags": ["int8", "Intel\u00ae Neural Compressor", "PostTrainingStatic"], "datasets": ["squad"], "metrics": ["f1"]}
transformers pytorch distilbert question-answering int8 Intel® Neural Compressor PostTrainingStatic dataset:squad license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #question-answering #int8 #Intel® Neural Compressor #PostTrainingStatic #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us 

INT8 DistilBERT base cased finetuned on Squad
=============================================


### 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 distilbert-base-cased-distilled-squad.


The calibration dataloader is the train dataloader. The default calibration sampling size 300 isn't divisible exactly by batch size 8, so the real sampling size is 304.


The linear module URL.1.ffn.lin2 falls back to fp32 to meet the 1% relative accuracy loss.


### Test result



### Load with optimum:



### 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 distilbert-base-cased-distilled-squad.


The calibration dataloader is the train dataloader. The default calibration sampling size 300 isn't divisible exactly by batch size 8, so the real sampling size is 304.


The linear module URL.1.ffn.lin2 falls back to fp32 to meet the 1% relative accuracy loss. ### Test result ### Load with optimum:
TAGS
#transformers #pytorch #distilbert #question-answering #int8 #Intel® Neural Compressor #PostTrainingStatic #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us 
 ### 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 distilbert-base-cased-distilled-squad.


The calibration dataloader is the train dataloader. The default calibration sampling size 300 isn't divisible exactly by batch size 8, so the real sampling size is 304.


The linear module URL.1.ffn.lin2 falls back to fp32 to meet the 1% relative accuracy loss. ### Test result ### Load with optimum:
passage: TAGS
#transformers #pytorch #distilbert #question-answering #int8 #Intel® Neural Compressor #PostTrainingStatic #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us 
### 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 distilbert-base-cased-distilled-squad.


The calibration dataloader is the train dataloader. The default calibration sampling size 300 isn't divisible exactly by batch size 8, so the real sampling size is 304.


The linear module URL.1.ffn.lin2 falls back to fp32 to meet the 1% relative accuracy loss.### Test result### Load with optimum: