Token Classification
Transformers
Safetensors
English
bert
named-entity-recognition
biomedical-nlp
protein-interactions
molecular-biology
biochemistry
systems-biology
protein
protein_complex
protein_enum
protein_familiy_or_group
protein_variant
Instructions to use OpenMed/OpenMed-NER-ProteinDetect-BioMed-335M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-ProteinDetect-BioMed-335M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-ProteinDetect-BioMed-335M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-ProteinDetect-BioMed-335M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-ProteinDetect-BioMed-335M") - Notebooks
- Google Colab
- Kaggle
docs: add citation info to README
Browse files
README.md
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Follow [OpenMed Org](https://huggingface.co/OpenMed) on Hugging Face 🤗 and click "Watch" to stay updated on our latest releases and developments.
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Follow [OpenMed Org](https://huggingface.co/OpenMed) on Hugging Face 🤗 and click "Watch" to stay updated on our latest releases and developments.
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## Citation
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If you use this model in your research or applications, please cite the following paper:
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```latex
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@misc{panahi2025openmedneropensourcedomainadapted,
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title={OpenMed NER: Open-Source, Domain-Adapted State-of-the-Art Transformers for Biomedical NER Across 12 Public Datasets},
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author={Maziyar Panahi},
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year={2025},
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eprint={2508.01630},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2508.01630},
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}
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```
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Proper citation helps support and acknowledge my work. Thank you!
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