onnx-community/biomedical-ner-all-ONNX
By onnx-community · ↓ 13 · ♥ 0 · Updated 2026-01-20
What's in this repo
This page lists the files in onnx-community/biomedical-ner-all-ONNX, including model.onnx, model_bnb4.onnx, model_fp16.onnx and more.
- 8 ONNX models (.onnx files)
- 16 files, including 8 .onnx, 5 .json, 1 .md, 1 .txt
- Last updated 2026-01-20
ONNX files
| File | Size | Download |
|---|---|---|
| onnx/model.onnx | — | Download |
| onnx/model_bnb4.onnx | — | Download |
| onnx/model_fp16.onnx | — | Download |
| onnx/model_int8.onnx | — | Download |
| onnx/model_q4.onnx | — | Download |
| onnx/model_q4f16.onnx | — | Download |
| onnx/model_quantized.onnx | — | Download |
| onnx/model_uint8.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
biomedical-ner-all (ONNX)
This is an ONNX version of d4data/biomedical-ner-all. It was automatically converted and uploaded using this Hugging Face Space.
Usage with Transformers.js
See the pipeline documentation for token-classification: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.TokenClassificationPipeline
About the Model
An English Named Entity Recognition model, trained on Maccrobat to recognize the bio-medical entities (107 entities) from a given text corpus (case reports etc.). This model was built on top of distilbert-base-uncased
- Dataset: Maccrobat https://figshare.com/articles/dataset/MACCROBAT2018/9764942
- Carbon emission: 0.0279399890043426 Kg
- Training time: 30.16527 minutes
- GPU used : 1 x GeForce RTX 3060 Laptop GPU
Checkout the tutorial video for explanation of this model and corresponding python library: https://youtu.be/xpiDPdBpS18
Usage
The easiest way is to load the inference api from huggingface and second method is through the pipeline object offered by transformers library.
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("d4data/biomedical-ner-all")
model = AutoModelForTokenClassification.from_pretrained("d4data/biomedical-ner-all")
pipe = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple") # pass device=0 if using gpu
pipe("""The patient reported no recurrence of palpitations at follow-up 6 months after the ablation.""")
Author
This model is part of the Research topic "AI in Biomedical field" conducted by Deepak John Reji, Shaina Raza. If you use this work (code, model or dataset), please star at:
https://github.com/dreji18/Bio-Epidemiology-NER
You can support me here :)
Model info
Created: 2026-01-20
Updated: 2026-01-20
View on Hugging Face