Feature Extraction
Transformers
Safetensors
English
bert
fill-mask
biomedical-text
nlp
biomedical-nlp
discharge-notes
healthcare
pubmed
text-embeddings-inference
Instructions to use Simonlee711/Clinical_ModernBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Simonlee711/Clinical_ModernBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Simonlee711/Clinical_ModernBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Simonlee711/Clinical_ModernBERT") model = AutoModelForMaskedLM.from_pretrained("Simonlee711/Clinical_ModernBERT", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Simonlee711/Clinical_ModernBERT: direct link, hf CLI and curl.
- Browser
- Download file 3.58 MB
-
https://huggingface.co/Simonlee711/Clinical_ModernBERT/resolve/main/tokenizer.json
- Command line
-
hf download hf://Simonlee711/Clinical_ModernBERT/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Simonlee711/Clinical_ModernBERT/resolve/main/tokenizer.json
3.58 MB
File too large to display, you can check the raw version instead.