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license: apache-2.0
tags:
- onnx
- int8
- quantized
- biomedical
- embeddings
- sentence-transformers
- justembed
base_model: cambridgeltl/SapBERT-from-PubMedBERT-fulltext
library_name: onnxruntime
pipeline_tag: feature-extraction
---
# SapBERT INT8 — ONNX Quantized
ONNX INT8 quantized version of [cambridgeltl/SapBERT-from-PubMedBERT-fulltext](https://huggingface.co/cambridgeltl/SapBERT-from-PubMedBERT-fulltext) for efficient biomedical entity embeddings.
## Model Details
| Property | Value |
|----------|-------|
| Base Model | [cambridgeltl/SapBERT-from-PubMedBERT-fulltext](https://huggingface.co/cambridgeltl/SapBERT-from-PubMedBERT-fulltext) |
| Format | ONNX |
| Quantization | INT8 (dynamic quantization) |
| Embedding Dimension | 768 |
| Quantized by | [JustEmbed](https://pypi.org/project/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
```python
from justembed import Embedder
embedder = Embedder("sapbert-int8")
vectors = embedder.embed(["aspirin", "acetylsalicylic acid"])
```
## Usage with ONNX Runtime
```python
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](https://huggingface.co/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](LICENSE) file for the full text.
## Citation
```bibtex
@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](https://github.com/cambridgeltl/sapbert)
- Quantization and packaging by [JustEmbed](https://pypi.org/project/justembed/)
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