DistilBERT
Collection
Smaller BERT models for question answering and text classification
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13 items
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Updated
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 import INCModelForTokenClassification
model_id = "Intel/distilbert-base-uncased-finetuned-conll03-english-int8-static"
int8_model = INCModelForTokenClassification.from_pretrained(model_id)
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