sekarkrishna/sapbert-int8

SapBERT INT8 — ONNX Quantized

ONNX INT8 quantized version of cambridgeltl/SapBERT-from-PubMedBERT-fulltext for efficient biomedical entity embeddings.

Model Details

Property	Value
Base Model	cambridgeltl/SapBERT-from-PubMedBERT-fulltext
Format	ONNX
Quantization	INT8 (dynamic quantization)
Embedding Dimension	768
Quantized by	JustEmbed

What is this?

This is a quantized ONNX export of SapBERT, a biomedical entity linking model trained on UMLS concepts. The INT8 quantization reduces model size and improves inference speed while maintaining high accuracy for biomedical text embeddings.

SapBERT (Self-Alignment Pre-training for BERT) was developed by the Cambridge Language Technology Lab for biomedical entity representation learning.

Use Cases

• Medical entity linking
• Biomedical concept matching
• Clinical terminology normalization
• Drug name standardization
• Disease concept mapping

Files

• model_quantized.onnx — INT8 quantized ONNX model
• tokenizer.json — Fast tokenizer
• config.json — Model configuration

Usage with JustEmbed

from justembed import Embedder

embedder = Embedder("sapbert-int8")
vectors = embedder.embed(["aspirin", "acetylsalicylic acid"])

Usage with ONNX Runtime

import onnxruntime as ort
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained(".")
session = ort.InferenceSession("model_quantized.onnx")

inputs = tokenizer("aspirin", return_tensors="np")
outputs = session.run(None, dict(inputs))

Quantization Details

• Method: Dynamic INT8 quantization via ONNX Runtime
• Source: Original PyTorch weights converted to ONNX, then quantized
• Accuracy: ~95%+ of FP32 performance on biomedical benchmarks
• Speed: ~2-3x faster inference than FP32
• Size: ~4x smaller than FP32

License

This model is a derivative work of cambridgeltl/SapBERT-from-PubMedBERT-fulltext.

The original model is licensed under Apache License 2.0. This quantized version is distributed under the same license. See the LICENSE file for the full text.

Citation

@inproceedings{liu2021self,
  title={Self-Alignment Pretraining for Biomedical Entity Representations},
  author={Liu, Fangyu and Shareghi, Ehsan and Meng, Zaiqiao and Basaldella, Marco and Collier, Nigel},
  booktitle={Proceedings of NAACL},
  year={2021}
}

Acknowledgments

• Original model by the Cambridge Language Technology Lab
• Quantization and packaging by JustEmbed