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_translations_only with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DT4H/CardioBERTa.ro_GP_translations_only with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="DT4H/CardioBERTa.ro_GP_translations_only")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("DT4H/CardioBERTa.ro_GP_translations_only") model = AutoModel.from_pretrained("DT4H/CardioBERTa.ro_GP_translations_only", 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 | |
| synonyms,70817,70817,70817,69316,70439,139248,70725,1.9985314260700113,2,0,0,0,0 | |
| parents,1607064,1606531,476350,376424,422977,531693,1591016,3.939225359504566,3.0,405533,0,392445,0 | |
| grandparents,4734361,4733224,476970,444901,470722,531980,4680229,9.850799840660839,7.0,406153,0,392732,0 | |