biomedical-ner-all · Model Card

biomedical-ner-all: Model Card

Written by D 4 Data Community, published under apache-2.0, revision 015a4050c9ac, read 2026-09-18. Shown as written; SAVRN's own facts about this model are on its page.

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

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