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:
- 9a184b2b11046834b0cd331a6add8c60f316cd5fc20f545baf507182cfc22a21
- Size of remote file:
- 17.1 MB
- SHA256:
- 3a56def25aa40facc030ea8b0b87f3688e4b3c39eb8b45d5702b3a1300fe2a20
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.