# INT8 bert-base-uncased-finetuned-swag

###  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 [thyagosme/bert-base-uncased-finetuned-swag](https://huggingface.co/thyagosme/bert-base-uncased-finetuned-swag).

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.

The linear modules **bert.encoder.layer.2.output.dense, bert.encoder.layer.5.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-accuracy)** |0.7838|0.7915|
| **Model size (MB)**  |133|418|

### Load with optimum:

```python
from optimum.intel import INCModelForMultipleChoice

model_id = "Intel/bert-base-uncased-finetuned-swag-int8-static"
int8_model = INCModelForMultipleChoice.from_pretrained(model_id)
```

{"language": ["en"], "license": "apache-2.0", "tags": ["multiple-choice", "int8", "Intel\u00ae Neural Compressor", "PostTrainingStatic"], "datasets": ["swag"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-finetuned-swag-int8-static", "results": [{"task": {"type": "multiple-choice", "name": "Multiple-choice"}, "dataset": {"name": "Swag", "type": "swag"}, "metrics": [{"type": "accuracy", "value": 0.7838148474693298, "name": "Accuracy"}]}]}]}
transformers pytorch bert multiple-choice int8 Intel® Neural Compressor PostTrainingStatic en dataset:swag license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #multiple-choice #int8 #Intel® Neural Compressor #PostTrainingStatic #en #dataset-swag #license-apache-2.0 #model-index #endpoints_compatible #region-us 

INT8 bert-base-uncased-finetuned-swag
=====================================


### 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 thyagosme/bert-base-uncased-finetuned-swag.


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.


The linear modules URL.2.URL, URL.5.URL, URL.9.URL, URL fall 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 thyagosme/bert-base-uncased-finetuned-swag.


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.


The linear modules URL.2.URL, URL.5.URL, URL.9.URL, URL fall back to fp32 to meet the 1% relative accuracy loss. ### Test result ### Load with optimum:
TAGS
#transformers #pytorch #bert #multiple-choice #int8 #Intel® Neural Compressor #PostTrainingStatic #en #dataset-swag #license-apache-2.0 #model-index #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 thyagosme/bert-base-uncased-finetuned-swag.


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.


The linear modules URL.2.URL, URL.5.URL, URL.9.URL, URL fall back to fp32 to meet the 1% relative accuracy loss. ### Test result ### Load with optimum:
passage: TAGS
#transformers #pytorch #bert #multiple-choice #int8 #Intel® Neural Compressor #PostTrainingStatic #en #dataset-swag #license-apache-2.0 #model-index #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 thyagosme/bert-base-uncased-finetuned-swag.


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.


The linear modules URL.2.URL, URL.5.URL, URL.9.URL, URL fall back to fp32 to meet the 1% relative accuracy loss.### Test result### Load with optimum: