
# INT8 RoBERT base finetuned on Squad2

### 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 [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2).

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 **roberta.encoder.layer.7.output.dense**, **roberta.encoder.layer.8.output.dense**, **roberta.encoder.layer.9.output.dense**, fall back to fp32 for less than 1% relative accuracy loss.

### Evaluation result

|   |INT8|FP32|
|---|:---:|:---:|
| **Accuracy (eval-f1)** |82.3122|82.9231|
| **Model size (MB)**  |141|474|

### Load with optimum:

```python
from optimum.intel.neural_compressor.quantization import IncQuantizedModelForQuestionAnswering
int8_model = IncQuantizedModelForQuestionAnswering.from_pretrained(
    'Intel/roberta-base-squad2-int8-static',
)
```

{"license": "cc-by-4.0", "tags": ["int8", "Intel\u00ae Neural Compressor", "PostTrainingStatic"], "datasets": ["squad2"], "metrics": ["f1"]}
transformers pytorch int8 Intel® Neural Compressor PostTrainingStatic dataset:squad2 license:cc-by-4.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #int8 #Intel® Neural Compressor #PostTrainingStatic #dataset-squad2 #license-cc-by-4.0 #endpoints_compatible #region-us 

INT8 RoBERT base finetuned on Squad2
====================================


### 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 deepset/roberta-base-squad2.


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.7.URL, URL.8.URL, URL.9.URL, fall back to fp32 for less than 1% relative accuracy loss.


### Evaluation 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 deepset/roberta-base-squad2.


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.7.URL, URL.8.URL, URL.9.URL, fall back to fp32 for less than 1% relative accuracy loss. ### Evaluation result ### Load with optimum:
TAGS
#transformers #pytorch #int8 #Intel® Neural Compressor #PostTrainingStatic #dataset-squad2 #license-cc-by-4.0 #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 deepset/roberta-base-squad2.


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.7.URL, URL.8.URL, URL.9.URL, fall back to fp32 for less than 1% relative accuracy loss. ### Evaluation result ### Load with optimum:
passage: TAGS
#transformers #pytorch #int8 #Intel® Neural Compressor #PostTrainingStatic #dataset-squad2 #license-cc-by-4.0 #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 deepset/roberta-base-squad2.


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.7.URL, URL.8.URL, URL.9.URL, fall back to fp32 for less than 1% relative accuracy loss.### Evaluation result### Load with optimum: