Feature Extraction
Transformers
Safetensors
Czech
roberta
biomedical
clinical
cardiology
entity-linking
concept-normalization
umls
metric-learning
text-embeddings-inference
Instructions to use DT4H/CardioBERTa.cs_GP_enriched with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DT4H/CardioBERTa.cs_GP_enriched with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="DT4H/CardioBERTa.cs_GP_enriched")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("DT4H/CardioBERTa.cs_GP_enriched") model = AutoModel.from_pretrained("DT4H/CardioBERTa.cs_GP_enriched", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from DT4H/CardioBERTa.cs_GP_enriched: direct link, hf CLI and curl.
- Browser
- Download file 3.94 MB
-
https://huggingface.co/DT4H/CardioBERTa.cs_GP_enriched/resolve/main/tokenizer.json
- Command line
-
hf download hf://DT4H/CardioBERTa.cs_GP_enriched/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/DT4H/CardioBERTa.cs_GP_enriched/resolve/main/tokenizer.json
3.94 MB
File too large to display, you can check the raw version instead.