File size: 1,971 Bytes
a212970 857d28d a212970 857d28d a212970 857d28d 01e2dd7 857d28d 33749c1 857d28d e3069f5 857d28d ec9cbc8 857d28d ec9cbc8 857d28d 9b30774 70a58f5 857d28d 33749c1 01e2dd7 33749c1 01e2dd7 33749c1 9b30774 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 |
---
language: en
license: mit
tags:
- text-classfication
- int8
- Intel® Neural Compressor
- PostTrainingStatic
datasets:
- mrpc
metrics:
- f1
---
# INT8 MiniLM-L12-H384 finetuned MRPC
## Post-training static quantization
### PyTorch
This is an INT8 PyTorch model quantized with [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor).
The original fp32 model comes from the fine-tuned model [Intel/MiniLM-L12-H384-uncased-mrpc](https://huggingface.co/Intel/MiniLM-L12-H384-uncased-mrpc).
The calibration dataloader is the train dataloader. The default calibration sampling size 100 isn't divisible exactly by batch size 8, so the real sampling size is 104.
The linear module **bert.encoder.layer.6.attention.self.key** falls back to fp32 to meet the 1% relative accuracy loss.
#### Test result
| |INT8|FP32|
|---|:---:|:---:|
| **Accuracy (eval-f1)** |0.9039|0.9097|
| **Model size (MB)** |33.5|127|
#### Load with optimum:
```python
from optimum.intel import INCModelForSequenceClassification
model_id = "Intel/MiniLM-L12-H384-uncased-mrpc-int8-static"
int8_model = INCModelForSequenceClassification.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 [Intel/MiniLM-L12-H384-uncased-mrpc](https://huggingface.co/Intel/MiniLM-L12-H384-uncased-mrpc).
The calibration dataloader is the eval dataloader. The calibration sampling size is 100.
#### Test result
| |INT8|FP32|
|---|:---:|:---:|
| **Accuracy (eval-f1)** |0.9013|0.9097|
| **Model size (MB)** |33|128|
#### Load ONNX model:
```python
from optimum.onnxruntime import ORTModelForSequenceClassification
model = ORTModelForSequenceClassification.from_pretrained('Intel/MiniLM-L12-H384-uncased-mrpc-int8-static')
```
|