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
- Xet hash:
- 0c15d22b818b4c746a486ce5817399c2dd748da98871bb0d1f90cb372f5dee3e
- Size of remote file:
- 1.11 GB
- SHA256:
- dd2252bff791693926d8c5d353c1fe222ff8f3310dece595f5c61ce0c9551bca
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