Feature Extraction
Transformers
Safetensors
English
roberta
biomedical
clinical
cardiology
entity-linking
concept-normalization
umls
metric-learning
text-embeddings-inference
Instructions to use DT4H/CardioBERTa.en_GP_only_snomed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DT4H/CardioBERTa.en_GP_only_snomed with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="DT4H/CardioBERTa.en_GP_only_snomed")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("DT4H/CardioBERTa.en_GP_only_snomed") model = AutoModel.from_pretrained("DT4H/CardioBERTa.en_GP_only_snomed", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| strategy,triplets,unique_triplets,unique_cuis,unique_anchors,unique_positives,unique_terms,unique_anchor_positive_pairs,terms_per_cui_mean,terms_per_cui_median,cuis_added_vs_synonyms,cuis_missing_vs_synonyms,terms_added_vs_synonyms,terms_missing_vs_synonyms | |
| parents,1182767,1182762,477285,315273,355267,470264,1175364,3.478097991765926,3,,,, | |
| grandparents,3760671,3760637,477292,385676,406841,470264,3740672,8.879103358112015,6.0,,,, | |