# INT8 albert-base-v2-sst2

##  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 [Alireza1044/albert-base-v2-sst2](https://huggingface.co/Alireza1044/albert-base-v2-sst2).

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 **albert.encoder.albert_layer_groups.0.albert_layers.0.ffn_output.module, albert.encoder.albert_layer_groups.0.albert_layers.0.ffn.module** fall back to fp32 to meet the 1% relative accuracy loss.

#### Test result

|   |INT8|FP32|
|---|:---:|:---:|
| **Accuracy (eval-accuracy)** |0.9255|0.9232|
| **Model size (MB)**  |25|44.6|

#### Load with Intel® Neural Compressor:

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

model_id = "Intel/albert-base-v2-sst2-int8-static"
int8_model = IncQuantizedModelForSequenceClassification.from_pretrained(model_id)
```

### 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 [Alireza1044/albert-base-v2-sst2](https://huggingface.co/Alireza1044/albert-base-v2-sst2).

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

#### Test result

|   |INT8|FP32|
|---|:---:|:---:|
| **Accuracy (eval-accuracy)** |0.9140|0.9232|
| **Model size (MB)**  |50|45|


#### Load ONNX model:

```python
from optimum.onnxruntime import ORTModelForSequenceClassification
model = ORTModelForSequenceClassification.from_pretrained('Intel/albert-base-v2-sst2-int8-static')
```
{"language": ["en"], "license": "apache-2.0", "tags": ["text-classfication", "int8", "Intel\u00ae Neural Compressor", "neural-compressor", "PostTrainingStatic"], "datasets": ["glue"], "metrics": ["accuracy"], "model_index": [{"name": "sst2", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "GLUE SST2", "type": "glue", "args": "sst2"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9254587155963303}}]}]}
transformers pytorch onnx albert text-classification text-classfication int8 Intel® Neural Compressor neural-compressor PostTrainingStatic en dataset:glue license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #onnx #albert #text-classification #text-classfication #int8 #Intel® Neural Compressor #neural-compressor #PostTrainingStatic #en #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

INT8 albert-base-v2-sst2
========================


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 Alireza1044/albert-base-v2-sst2.


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 albert.encoder.albert\_layer\_groups.0.albert\_layers.0.ffn\_output.module, albert.encoder.albert\_layer\_groups.0.albert\_layers.0.URL fall back to fp32 to meet the 1% relative accuracy loss.


#### 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 Alireza1044/albert-base-v2-sst2.


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 Alireza1044/albert-base-v2-sst2.


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 albert.encoder.albert\_layer\_groups.0.albert\_layers.0.ffn\_output.module, albert.encoder.albert\_layer\_groups.0.albert\_layers.0.URL fall back to fp32 to meet the 1% relative accuracy loss. #### 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 Alireza1044/albert-base-v2-sst2.


The calibration dataloader is the eval dataloader. The calibration sampling size is 100. #### Test result #### Load ONNX model:
TAGS
#transformers #pytorch #onnx #albert #text-classification #text-classfication #int8 #Intel® Neural Compressor #neural-compressor #PostTrainingStatic #en #dataset-glue #license-apache-2.0 #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 Alireza1044/albert-base-v2-sst2.


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 albert.encoder.albert\_layer\_groups.0.albert\_layers.0.ffn\_output.module, albert.encoder.albert\_layer\_groups.0.albert\_layers.0.URL fall back to fp32 to meet the 1% relative accuracy loss. #### 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 Alireza1044/albert-base-v2-sst2.


The calibration dataloader is the eval dataloader. The calibration sampling size is 100. #### Test result #### Load ONNX model:
passage: TAGS
#transformers #pytorch #onnx #albert #text-classification #text-classfication #int8 #Intel® Neural Compressor #neural-compressor #PostTrainingStatic #en #dataset-glue #license-apache-2.0 #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 Alireza1044/albert-base-v2-sst2.


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 albert.encoder.albert\_layer\_groups.0.albert\_layers.0.ffn\_output.module, albert.encoder.albert\_layer\_groups.0.albert\_layers.0.URL fall back to fp32 to meet the 1% relative accuracy loss.#### 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 Alireza1044/albert-base-v2-sst2.


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