
# Dynamically quantized DistilBERT base uncased finetuned SST-2

## Table of Contents
- [Model Details](#model-details)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)

## Model Details
**Model Description:** This model is a [DistilBERT](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) fine-tuned on SST-2 dynamically quantized with [optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor). 
- **Model Type:** Text Classification
- **Language(s):** English
- **License:** Apache-2.0
- **Parent Model:** For more details on the original model, we encourage users to check out [this](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) model card.

## How to Get Started With the Model

### PyTorch

To load the quantized model, you can do as follows:

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

model = IncQuantizedModelForSequenceClassification.from_pretrained("Intel/distilbert-base-uncased-finetuned-sst-2-english-int8-dynamic")
```

### 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 [DistilBERT](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english).

#### Test result

|   |INT8|FP32|
|---|:---:|:---:|
| **Accuracy (eval-accuracy)** |0.9025|0.9106|
| **Model size (MB)**  |165|256|

#### Load ONNX model:

```python
from optimum.onnxruntime import ORTModelForSequenceClassification
model = ORTModelForSequenceClassification.from_pretrained('Intel/distilbert-base-uncased-finetuned-sst-2-english-int8-dynamic')
```

{"language": "en", "license": "apache-2.0", "tags": ["neural-compressor", "text-classfication", "int8", "8-bit", "onnx", "Intel\u00ae Neural Compressor"], "datasets": ["sst2", "glue"], "metrics": ["accuracy"]}
transformers pytorch onnx distilbert text-classification neural-compressor text-classfication int8 8-bit Intel® Neural Compressor en dataset:sst2 dataset:glue license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #onnx #distilbert #text-classification #neural-compressor #text-classfication #int8 #8-bit #Intel® Neural Compressor #en #dataset-sst2 #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

Dynamically quantized DistilBERT base uncased finetuned SST-2
=============================================================


Table of Contents
-----------------


* Model Details
* How to Get Started With the Model


Model Details
-------------


Model Description: This model is a DistilBERT fine-tuned on SST-2 dynamically quantized with optimum-intel through the usage of huggingface/optimum-intel through the usage of Intel® Neural Compressor.


* Model Type: Text Classification
* Language(s): English
* License: Apache-2.0
* Parent Model: For more details on the original model, we encourage users to check out this model card.


How to Get Started With the Model
---------------------------------


### PyTorch


To load the quantized model, you can do as follows:


### ONNX


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


The original fp32 model comes from the fine-tuned model DistilBERT.


#### Test result



#### Load ONNX model:



### PyTorch


To load the quantized model, you can do as follows: ### ONNX


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


The original fp32 model comes from the fine-tuned model DistilBERT. #### Test result #### Load ONNX model:
TAGS
#transformers #pytorch #onnx #distilbert #text-classification #neural-compressor #text-classfication #int8 #8-bit #Intel® Neural Compressor #en #dataset-sst2 #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### PyTorch


To load the quantized model, you can do as follows: ### ONNX


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


The original fp32 model comes from the fine-tuned model DistilBERT. #### Test result #### Load ONNX model:
passage: TAGS
#transformers #pytorch #onnx #distilbert #text-classification #neural-compressor #text-classfication #int8 #8-bit #Intel® Neural Compressor #en #dataset-sst2 #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### PyTorch


To load the quantized model, you can do as follows:### ONNX


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


The original fp32 model comes from the fine-tuned model DistilBERT.#### Test result#### Load ONNX model: