
# INT8 MiniLM-L12-H384 finetuned MRPC

## Post-training static quantization

### PyTorch

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 [Intel/MiniLM-L12-H384-uncased-mrpc](https://huggingface.co/Intel/MiniLM-L12-H384-uncased-mrpc).

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 module **bert.encoder.layer.6.attention.self.key** falls back to fp32 to meet the 1% relative accuracy loss.

#### Test result

|   |INT8|FP32|
|---|:---:|:---:|
| **Accuracy (eval-f1)** |0.9039|0.9097|
| **Model size (MB)**  |33.5|127|

#### Load with optimum:

```python
from optimum.intel.neural_compressor.quantization import IncQuantizedModelForSequenceClassification 
int8_model = IncQuantizedModelForSequenceClassification(
    'Intel/MiniLM-L12-H384-uncased-mrpc-int8-static',
)
```

### ONNX

This is an INT8 ONNX model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor).

The original fp32 model comes from the fine-tuned model [Intel/MiniLM-L12-H384-uncased-mrpc](https://huggingface.co/Intel/MiniLM-L12-H384-uncased-mrpc).

The calibration dataloader is the eval dataloader. The calibration sampling size is 100.

#### Test result

|   |INT8|FP32|
|---|:---:|:---:|
| **Accuracy (eval-f1)** |0.9013|0.9097|
| **Model size (MB)**  |33|128|


#### Load ONNX model:

```python
from optimum.onnxruntime import ORTModelForSequenceClassification
model = ORTModelForSequenceClassification.from_pretrained('Intel/MiniLM-L12-H384-uncased-mrpc-int8-static')
```
{"language": "en", "license": "mit", "tags": ["text-classfication", "int8", "Intel\u00ae Neural Compressor", "PostTrainingStatic"], "datasets": ["mrpc"], "metrics": ["f1"]}
transformers pytorch onnx bert text-classification text-classfication int8 Intel® Neural Compressor PostTrainingStatic en dataset:mrpc license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #onnx #bert #text-classification #text-classfication #int8 #Intel® Neural Compressor #PostTrainingStatic #en #dataset-mrpc #license-mit #autotrain_compatible #endpoints_compatible #region-us 

INT8 MiniLM-L12-H384 finetuned MRPC
===================================


Post-training static quantization
---------------------------------


### PyTorch


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 Intel/MiniLM-L12-H384-uncased-mrpc.


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 module URL.6.URL falls back to fp32 to meet the 1% relative accuracy loss.


#### Test result



#### Load with optimum:


### ONNX


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


The original fp32 model comes from the fine-tuned model Intel/MiniLM-L12-H384-uncased-mrpc.


The calibration dataloader is the eval dataloader. The calibration sampling size is 100.


#### Test result



#### Load ONNX model:



### PyTorch


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 Intel/MiniLM-L12-H384-uncased-mrpc.


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 module URL.6.URL falls back to fp32 to meet the 1% relative accuracy loss. #### Test result #### Load with optimum: ### ONNX


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


The original fp32 model comes from the fine-tuned model Intel/MiniLM-L12-H384-uncased-mrpc.


The calibration dataloader is the eval dataloader. The calibration sampling size is 100. #### Test result #### Load ONNX model:
TAGS
#transformers #pytorch #onnx #bert #text-classification #text-classfication #int8 #Intel® Neural Compressor #PostTrainingStatic #en #dataset-mrpc #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### PyTorch


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 Intel/MiniLM-L12-H384-uncased-mrpc.


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 module URL.6.URL falls back to fp32 to meet the 1% relative accuracy loss. #### Test result #### Load with optimum: ### ONNX


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


The original fp32 model comes from the fine-tuned model Intel/MiniLM-L12-H384-uncased-mrpc.


The calibration dataloader is the eval dataloader. The calibration sampling size is 100. #### Test result #### Load ONNX model:
passage: TAGS
#transformers #pytorch #onnx #bert #text-classification #text-classfication #int8 #Intel® Neural Compressor #PostTrainingStatic #en #dataset-mrpc #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### PyTorch


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 Intel/MiniLM-L12-H384-uncased-mrpc.


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 module URL.6.URL falls back to fp32 to meet the 1% relative accuracy loss.#### Test result#### Load with optimum:### ONNX


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


The original fp32 model comes from the fine-tuned model Intel/MiniLM-L12-H384-uncased-mrpc.


The calibration dataloader is the eval dataloader. The calibration sampling size is 100.#### Test result#### Load ONNX model: