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-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thomas-sounack/BioClinical-ModernBERT-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="thomas-sounack/BioClinical-ModernBERT-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("thomas-sounack/BioClinical-ModernBERT-base") model = AutoModelForMaskedLM.from_pretrained("thomas-sounack/BioClinical-ModernBERT-base") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 23feb516b4dd1d67c951a76deae601ddeb6900c6dfe94ff5e072debb05145389
- Size of remote file:
- 599 MB
- SHA256:
- 763224fa377f2fdbf4cf2ed82a2a0cb6e79779bb5eb4720e430a21c46783d7e8
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