Fill-Mask
Transformers
PyTorch
Safetensors
English
modernbert
masked-lm
long-context
BioClinical-ModernBERT
clinical
biomedical
clinical encoder
clinical modern bert
Instructions to use thomas-sounack/BioClinical-ModernBERT-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thomas-sounack/BioClinical-ModernBERT-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="thomas-sounack/BioClinical-ModernBERT-large")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("thomas-sounack/BioClinical-ModernBERT-large") model = AutoModelForMaskedLM.from_pretrained("thomas-sounack/BioClinical-ModernBERT-large", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
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README.md
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## Citation
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If you use BioClinical ModernBERT in your work, please cite:
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## Citation
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If you use BioClinical ModernBERT in your work, please cite our preprint:
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```
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@misc{sounack2025bioclinicalmodernbertstateoftheartlongcontext,
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title={BioClinical ModernBERT: A State-of-the-Art Long-Context Encoder for Biomedical and Clinical NLP},
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author={Thomas Sounack and Joshua Davis and Brigitte Durieux and Antoine Chaffin and Tom J. Pollard and Eric Lehman and Alistair E. W. Johnson and Matthew McDermott and Tristan Naumann and Charlotta Lindvall},
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year={2025},
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eprint={2506.10896},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2506.10896},
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}
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```
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