Loading…
Loading…
Specialized model for Anatomical Entity Recognition - Anatomical structures and body parts
Tailored to anatomical structure recognition, including organs, tissues, and substructures in clinical narratives.Supports radiology and surgical note parsing, site-of-disease extraction, and anatomy-aware analytics.
OpenMed ZeroShot NER is an advanced, domain-adapted Named Entity Recognition (NER) model designed specifically for medical, biomedical, and clinical text mining. Leveraging state-of-the-art zero-shot learning, this model empowers researchers, clinicians, and data scientists to extract expert-level biomedical entities—such as diseases, chemicals, genes, species, and clinical findings—directly from unstructured text, without the need for task-specific retraining.
Built on the robust GLiNER architecture and fine-tuned on curated biomedical corpora, OpenMed ZeroShot NER delivers high-precision entity recognition for critical healthcare and life sciences applications. Its zero-shot capability means you can flexibly define and extract any entity type relevant to your workflow, from standard biomedical categories to custom clinical concepts, supporting rapid adaptation to new research domains and regulatory requirements.
Whether you are working on clinical NLP, biomedical research, electronic health record (EHR) de-identification, or large-scale literature mining, OpenMed ZeroShot NER provides a production-ready, open-source solution that combines expert-level accuracy with unmatched flexibility. Join the OpenMed community to accelerate your medical text analytics with cutting-edge, zero-shot NER technology.
This zero-shot model can identify and classify biomedical entities, including but not limited to these entity types. You can also add custom entity types without retraining the model:
Anatomy💡 Zero-Shot Flexibility: As a GliNER-based model, you can specify any entity types you want to detect, even if they weren't part of the original training. Simply provide the entity labels when using the model, and it will adapt to recognize them.
Anatomy corpus focuses on anatomical entity recognition for medical terminology and healthcare applications.
The Anatomy corpus is a specialized biomedical NER dataset designed for recognizing anatomical entities and medical terminology in clinical and biomedical texts. This corpus contains annotations for anatomical structures, body parts, organs, and physiological systems mentioned in medical literature. It is essential for developing clinical NLP systems, medical education tools, and healthcare informatics applications where accurate anatomical entity identification is crucial. The dataset supports the development of automated systems for medical coding, clinical decision support, and anatomical knowledge extraction from medical records and literature. It serves as a valuable resource for training NER models used in medical imaging, surgical planning, and clinical documentation.
0.91187.3%| Rank | Model | Base F1 | Finetuned F1 | ΔF1 | ΔF1 % |
|---|---|---|---|---|---|
| 🥇 1 | OpenMed-ZeroShot-NER-Anatomy-Large-459M | 0.2978 | 0.9271 | 0.6293 | 211.3% |
| 🥈 2 | OpenMed-ZeroShot-NER-Anatomy-Medium-209M | 0.3172 | 0.9114 | 0.5942 | 187.3% |
| 🥉 3 | OpenMed-ZeroShot-NER-Anatomy-XLarge-770M | 0.3780 | 0.9021 | 0.5241 | 138.7% |
| 4 | OpenMed-ZeroShot-NER-Anatomy-Base-220M | 0.2804 | 0.8627 | 0.5823 | 207.7% |
| 5 | OpenMed-ZeroShot-NER-Anatomy-Multi-209M | 0.3121 | 0.8091 | 0.4969 | 159.2% |
Rankings are sorted by finetuned F1 and show ΔF1% over base model. Test dataset is excluded from training.
Figure: OpenMed ZeroShot Clinical & Biomedical NER vs. Original GLiNER models.
pip install -q "gliner[tokenizers]"
from transformers import pipeline
# Load the model and tokenizer
# Model: https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Anatomy-Medium-209M
model_name = "OpenMed/OpenMed-ZeroShot-NER-Anatomy-Medium-209M"
from gliner import GLiNER
model = GLiNER.from_pretrained(model_name)
# Example usage with default entity types
text = "The patient complained of pain in the left ventricle region."
labels = ['Anatomy']
entities = model.predict_entities(text, labels, flat_ner=True, threshold=0.5)
for entity in entities:
print(entity)
💡 Tip: If you want to extract entities that are not present in the original training set (i.e., use custom or rare entity types), you may get better results by lowering the threshold parameter in model.predict_entities. For example, try threshold=0.3 or even lower, depending on your use case:
# You can specify custom entity types for zero-shot recognition - for instance:
custom_entities = ["MISC", "Anatomy", "PERSON", "LOCATION", "MEDICATION", "PROCEDURE"]
entities = model.predict_entities(text, custom_entities, flat_ner=True, threshold=0.1)
for entity in entities:
print(entity)
Lowering the threshold makes the model more permissive and can help it recognize new or less common entity types, but may also increase false positives. Adjust as needed for your application.
This model is particularly useful for:
Licensed under the Apache License 2.0. See LICENSE for details.
I welcome contributions of all kinds! Whether you have ideas, feature requests, or want to join my mission to advance open-source Healthcare AI, I'd love to hear from you.
Follow OpenMed Org on Hugging Face 🤗 and click "Watch" to stay updated on my latest releases and developments.
If you use this model in your research or applications, please cite the following paper:
@misc{panahi2025openmedneropensourcedomainadapted,
title={OpenMed NER: Open-Source, Domain-Adapted State-of-the-Art Transformers for Biomedical NER Across 12 Public Datasets},
author={Maziyar Panahi},
year={2025},
eprint={2508.01630},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2508.01630},
}
Proper citation helps support and acknowledge my work. Thank you!