transformers pytorch albert text-classification text-classfication nlp neural-compressor PostTrainingsDynamic int8 Intel® Neural Compressor en dataset:glue dataset:mrpc license:mit autotrain_compatible endpoints_compatible region:us
---
datasets:
- glue
- mrpc
language: en
license: mit
metrics:
- f1
tags:
- text-classfication
- nlp
- neural-compressor
- PostTrainingsDynamic
- int8
- Intel® Neural Compressor
- albert
---

# Dynamically quantized Albert base finetuned MPRC

## 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 [Albert](https://huggingface.co/textattack/albert-base-v2-MRPC) fine-tuned on MPRC 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/textattack/albert-base-v2-MRPC) model card.

## How to Get Started With the Model

### PyTorch

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

```python
from optimum.intel import INCModelForSequenceClassification

model = INCModelForSequenceClassification.from_pretrained("Intel/albert-base-v2-MRPC-int8")
```

#### Test result

|   |INT8|FP32|
|---|:---:|:---:|
| **Accuracy (eval-f1)** |0.9193|0.9263|
| **Model size (MB)**  |45.0|46.7|
