|
--- |
|
language: en |
|
license: apache-2.0 |
|
datasets: |
|
- sst2 |
|
- glue |
|
metrics: |
|
- accuracy |
|
tags: |
|
- text-classfication |
|
- int8 |
|
- onnx |
|
--- |
|
|
|
# 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-f1)** |0.9037|0.9106| |
|
| **Model size (MB)** |73|256| |
|
|
|
#### Load ONNX model: |
|
|
|
```python |
|
from optimum.onnxruntime import ORTModelForSequenceClassification |
|
model = ORTModelForSequenceClassification.from_pretrained('Intel/distilbert-base-uncased-finetuned-sst-2-english-int8-dynamic') |
|
``` |
|
|