Loading…
Loading…
Specialized model for Gene Entity Recognition - Gene-related entities
Targets gene and genetics entities, handling symbol/name variants commonly found in genomics literature.Useful for genetic association studies, variant curation, and genomics informatics.
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:
Cell-line-name💡 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.
Gellus corpus targets gene recognition and genetics entities for genomics and molecular biology applications.
The Gellus corpus is a biomedical NER dataset specifically designed for gene recognition and genetics entity extraction in molecular biology literature. This corpus contains comprehensive annotations for gene names, genetic variants, and genomics-related entities that are essential for genetic research and genomics applications. The dataset supports the development of automated systems for gene mention identification, genetic association studies, and genomics text mining. It is particularly valuable for identifying genes involved in hereditary diseases, genetic disorders, and molecular genetics research. The corpus serves as a benchmark for evaluating NER models used in genetics research, personalized medicine, and genomics informatics, contributing to advances in precision medicine and genetic counseling applications.
0.9779.9%| Rank | Model | Base F1 | Finetuned F1 | ΔF1 | ΔF1 % |
|---|---|---|---|---|---|
| 🥇 1 | OpenMed-ZeroShot-NER-Genomic-Large-459M | 0.5361 | 0.9775 | 0.4414 | 82.3% |
| 🥈 2 | OpenMed-ZeroShot-NER-Genomic-Medium-209M | 0.5376 | 0.9674 | 0.4298 | 79.9% |
| 🥉 3 | OpenMed-ZeroShot-NER-Genomic-XLarge-770M | 0.6875 | 0.9003 | 0.2128 | 30.9% |
| 4 | OpenMed-ZeroShot-NER-Genomic-Small-166M | 0.4694 | 0.8082 | 0.3388 | 72.2% |
| 5 | OpenMed-ZeroShot-NER-Genomic-Multi-209M | 0.4000 | 0.7333 | 0.3333 | 83.3% |
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-Genomic-Medium-209M
model_name = "OpenMed/OpenMed-ZeroShot-NER-Genomic-Medium-209M"
from gliner import GLiNER
model = GLiNER.from_pretrained(model_name)
# Example usage with default entity types
text = "The BRCA2 gene is associated with hereditary breast cancer."
labels = ['Cell-line-name']
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", "Cell-line-name", "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!