Intel/distilbert-base-uncased-finetuned-sst-2-english-int8-dynamic-inc
By Intel · ↓ 13 · ♥ 1 · Updated 2024-03-21
What's in this repo
This page lists the files in Intel/distilbert-base-uncased-finetuned-sst-2-english-int8-dynamic-inc, including model.onnx.
- 1 ONNX models (.onnx files)
- 9 files, including 3 .json, 1 .md, 1 .onnx, 1 .bin, 1 .yml, 1 .txt
- Last updated 2024-03-21
ONNX files
| File | Size | Download |
|---|---|---|
| model.onnx | — | Download |
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
README
Dynamically quantized DistilBERT base uncased finetuned SST-2
Table of Contents
Model Details
Model Description: This model is a DistilBERT fine-tuned on SST-2 dynamically quantized with optimum-intel through the usage of huggingface/optimum-intel through the usage of 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 model card.
How to Get Started With the Model
PyTorch
To load the quantized model, you can do as follows:
from optimum.intel import INCModelForSequenceClassification
model_id = "distilbert-base-uncased-finetuned-sst-2-english-int8-dynamic-inc"
model = INCModelForSequenceClassification.from_pretrained(model_id)
ONNX
This is an INT8 ONNX model quantized with Intel® Neural Compressor.
The original fp32 model comes from the fine-tuned model DistilBERT.
Test result
| INT8 | FP32 | |
|---|---|---|
| Accuracy (eval-accuracy) | 0.9025 | 0.9106 |
| Model size (MB) | 165 | 256 |
Load ONNX model:
from optimum.onnxruntime import ORTModelForSequenceClassification
model = ORTModelForSequenceClassification.from_pretrained('Intel/distilbert-base-uncased-finetuned-sst-2-english-int8-dynamic')
Model info
Created: 2022-08-02
Updated: 2024-03-21
View on Hugging Face