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
File size: 592 Bytes
189ae81 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | {
"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."
}
|