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
| { | |
| "language": "ro", | |
| "strategy": "grandparents", | |
| "triplets_type": "translations_only", | |
| "backbone": "DT4H/CardioBERTa.ro", | |
| "training": { | |
| "objective": "Multi-Similarity Loss", | |
| "pairwise": true, | |
| "miner": { | |
| "enabled": true, | |
| "type_of_triplets": "all", | |
| "margin": 0.2 | |
| }, | |
| "pooling": "cls", | |
| "epochs": 1, | |
| "batch_size": 256, | |
| "learning_rate": 2e-05, | |
| "max_length": 25, | |
| "random_seed": 33 | |
| }, | |
| "training_data_availability": "Training terminology is not redistributed because it contains resources subject to UMLS licensing conditions." | |
| } | |