Edit model card

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

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

Load with optimum:

from optimum.intel.neural_compressor.quantization import IncQuantizedModelForTokenClassification
int8_model = IncQuantizedModelForTokenClassification.from_pretrained(
    'Intel/distilbert-base-uncased-finetuned-conll03-english-int8-static',
)
Downloads last month
14
Hosted inference API
Token Classification
Examples
Examples
This model can be loaded on the Inference API on-demand.

Dataset used to train Intel/distilbert-base-uncased-finetuned-conll03-english-int8-static

Evaluation results