
# INT8 BERT base uncased finetuned on Squad

### 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 [jimypbr/bert-base-uncased-squad](https://huggingface.co/jimypbr/bert-base-uncased-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 modules **bert.encoder.layer.2.intermediate.dense**, **bert.encoder.layer.4.intermediate.dense**, **bert.encoder.layer.9.output.dense**, **bert.encoder.layer.10.output.dense** fall back to fp32 to meet the 1% relative accuracy loss.

### Test result

|   |INT8|FP32|
|---|:---:|:---:|
| **Accuracy (eval-f1)** |87.3006|88.1030|
| **Model size (MB)**  |139|436|

### Load with Intel® Neural Compressor:

```python
from optimum.intel import INCModelForQuestionAnswering

model_id = "Intel/bert-base-uncased-squad-int8-static"
int8_model = INCModelForQuestionAnswering.from_pretrained(model_id)
```

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

INT8 BERT base uncased finetuned on Squad
=========================================


### Post-training static quantization


This is an INT8 PyTorch model quantized with Intel® Neural Compressor.


The original fp32 model comes from the fine-tuned model jimypbr/bert-base-uncased-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 modules URL.2.URL, URL.4.URL, URL.9.URL, URL fall back to fp32 to meet the 1% relative accuracy loss.


### Test result



### Load with Intel® Neural Compressor:



### Post-training static quantization


This is an INT8 PyTorch model quantized with Intel® Neural Compressor.


The original fp32 model comes from the fine-tuned model jimypbr/bert-base-uncased-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 modules URL.2.URL, URL.4.URL, URL.9.URL, URL fall back to fp32 to meet the 1% relative accuracy loss. ### Test result ### Load with Intel® Neural Compressor:
TAGS
#transformers #pytorch #bert #question-answering #int8 #Intel® Neural Compressor #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 Intel® Neural Compressor.


The original fp32 model comes from the fine-tuned model jimypbr/bert-base-uncased-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 modules URL.2.URL, URL.4.URL, URL.9.URL, URL fall back to fp32 to meet the 1% relative accuracy loss. ### Test result ### Load with Intel® Neural Compressor:
passage: TAGS
#transformers #pytorch #bert #question-answering #int8 #Intel® Neural Compressor #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 Intel® Neural Compressor.


The original fp32 model comes from the fine-tuned model jimypbr/bert-base-uncased-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 modules URL.2.URL, URL.4.URL, URL.9.URL, URL fall back to fp32 to meet the 1% relative accuracy loss.### Test result### Load with Intel® Neural Compressor: