# INT8 xlnet-base-cased-mrpc

## Post-training static quantization

### PyTorch

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 [xlnet-base-cased-mrpc](https://huggingface.co/Intel/xlnet-base-cased-mrpc).

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.

#### Test result

|   |INT8|FP32|
|---|:---:|:---:|
| **Accuracy (eval-f1)** |0.8893|0.8897|
| **Model size (MB)**  |215|448|

#### Load with Intel® Neural Compressor:

```python
from optimum.intel.neural_compressor.quantization import IncQuantizedModelForSequenceClassification

int8_model = IncQuantizedModelForSequenceClassification.from_pretrained(
    "Intel/xlnet-base-cased-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 [xlnet-base-cased-mrpc](https://huggingface.co/Intel/xlnet-base-cased-mrpc).

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

#### Test result

|   |INT8|FP32|
|---|:---:|:---:|
| **Accuracy (eval-f1)** |0.8974|0.8986|
| **Model size (MB)**  |226|448|


#### Load ONNX model:

```python
from optimum.onnxruntime import ORTModelForSequenceClassification
model = ORTModelForSequenceClassification.from_pretrained('Intel/xlnet-base-cased-mrpc-int8-static')
```


{"language": ["en"], "license": "mit", "tags": ["text-classfication", "int8", "neural-compressor", "Intel\u00ae Neural Compressor", "PostTrainingStatic", "onnx"], "datasets": ["glue"], "metrics": ["f1"], "model-index": [{"name": "xlnet-base-cased-mrpc-int8-static", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MRPC", "type": "glue", "args": "mrpc"}, "metrics": [{"type": "f1", "value": 0.8892794376098417, "name": "F1"}]}]}]}
transformers pytorch onnx xlnet text-classification text-classfication int8 neural-compressor Intel® Neural Compressor PostTrainingStatic en dataset:glue license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #onnx #xlnet #text-classification #text-classfication #int8 #neural-compressor #Intel® Neural Compressor #PostTrainingStatic #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

INT8 xlnet-base-cased-mrpc
==========================


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


### PyTorch


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


The original fp32 model comes from the fine-tuned model xlnet-base-cased-mrpc.


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.


#### Test result



#### Load with Intel® Neural Compressor:


### ONNX


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


The original fp32 model comes from the fine-tuned model xlnet-base-cased-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 Intel® Neural Compressor.


The original fp32 model comes from the fine-tuned model xlnet-base-cased-mrpc.


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. #### Test result #### Load with Intel® Neural Compressor: ### ONNX


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


The original fp32 model comes from the fine-tuned model xlnet-base-cased-mrpc.


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


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


The original fp32 model comes from the fine-tuned model xlnet-base-cased-mrpc.


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. #### Test result #### Load with Intel® Neural Compressor: ### ONNX


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


The original fp32 model comes from the fine-tuned model xlnet-base-cased-mrpc.


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


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


The original fp32 model comes from the fine-tuned model xlnet-base-cased-mrpc.


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.#### Test result#### Load with Intel® Neural Compressor:### ONNX


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


The original fp32 model comes from the fine-tuned model xlnet-base-cased-mrpc.


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