Feature Extraction
Transformers
Safetensors
Romanian
xlm-roberta
biomedical
clinical
cardiology
entity-linking
concept-normalization
umls
metric-learning
text-embeddings-inference
Instructions to use DT4H/CardioBERTa.ro_GP_enriched with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DT4H/CardioBERTa.ro_GP_enriched with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="DT4H/CardioBERTa.ro_GP_enriched")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("DT4H/CardioBERTa.ro_GP_enriched") model = AutoModel.from_pretrained("DT4H/CardioBERTa.ro_GP_enriched", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 972 Bytes
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