Feature Extraction
Transformers
Safetensors
Spanish
roberta
biomedical
clinical
cardiology
entity-linking
concept-normalization
umls
metric-learning
text-embeddings-inference
Instructions to use DT4H/CardioBERTa.es_P_translations_only with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DT4H/CardioBERTa.es_P_translations_only with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="DT4H/CardioBERTa.es_P_translations_only")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("DT4H/CardioBERTa.es_P_translations_only") model = AutoModel.from_pretrained("DT4H/CardioBERTa.es_P_translations_only", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 588d9ebcc81cc4a9c8ee69dc536330b66ef328ee58ab019aa97d337d252f5102
- Size of remote file:
- 504 MB
- SHA256:
- f4d3fcd4a15acce54a6d4ceeb7fe7738902a0ca276a7a47d989decdddc2dce8b
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