# INT8 distilbert-base-uncased-finetuned-conll03-english

###  Post-training static quantization

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 [elastic/distilbert-base-uncased-finetuned-conll03-english](https://huggingface.co/elastic/distilbert-base-uncased-finetuned-conll03-english).

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.

### Test result

|   |INT8|FP32|
|---|:---:|:---:|
| **Accuracy (eval-accuracy)** |0.9859|0.9882|
| **Model size (MB)**  |64.5|253|

### Load with optimum:

```python
from optimum.intel import INCModelForTokenClassification

model_id = "Intel/distilbert-base-uncased-finetuned-conll03-english-int8-static"
int8_model = INCModelForTokenClassification.from_pretrained(model_id)
```

{"language": ["en"], "license": "apache-2.0", "tags": ["token-classfication", "int8", "Intel\u00ae Neural Compressor", "PostTrainingStatic"], "datasets": ["conll2003"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-conll03-english-int8-static", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "Conll2003", "type": "conll2003"}, "metrics": [{"type": "accuracy", "value": 0.9858650364082395, "name": "Accuracy"}]}]}]}
transformers pytorch distilbert token-classification token-classfication int8 Intel® Neural Compressor PostTrainingStatic en dataset:conll2003 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #token-classification #token-classfication #int8 #Intel® Neural Compressor #PostTrainingStatic #en #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

INT8 distilbert-base-uncased-finetuned-conll03-english
======================================================


### Post-training static quantization


This is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.


The original fp32 model comes from the fine-tuned model elastic/distilbert-base-uncased-finetuned-conll03-english.


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.


### Test result



### Load with optimum:



### Post-training static quantization


This is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.


The original fp32 model comes from the fine-tuned model elastic/distilbert-base-uncased-finetuned-conll03-english.


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. ### Test result ### Load with optimum:
TAGS
#transformers #pytorch #distilbert #token-classification #token-classfication #int8 #Intel® Neural Compressor #PostTrainingStatic #en #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Post-training static quantization


This is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.


The original fp32 model comes from the fine-tuned model elastic/distilbert-base-uncased-finetuned-conll03-english.


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. ### Test result ### Load with optimum:
passage: TAGS
#transformers #pytorch #distilbert #token-classification #token-classfication #int8 #Intel® Neural Compressor #PostTrainingStatic #en #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Post-training static quantization


This is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.


The original fp32 model comes from the fine-tuned model elastic/distilbert-base-uncased-finetuned-conll03-english.


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.### Test result### Load with optimum: