Feature Extraction
Transformers
Safetensors
English
roberta
biomedical
clinical
cardiology
entity-linking
concept-normalization
umls
metric-learning
text-embeddings-inference
Instructions to use DT4H/CardioBERTa.en_GP_only_snomed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DT4H/CardioBERTa.en_GP_only_snomed with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="DT4H/CardioBERTa.en_GP_only_snomed")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("DT4H/CardioBERTa.en_GP_only_snomed") model = AutoModel.from_pretrained("DT4H/CardioBERTa.en_GP_only_snomed", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 34cefa871aa9cf054f4442947e1fe782f2821380dd8a4770d3fec31bf36084f9
- Size of remote file:
- 499 MB
- SHA256:
- 29ea2cdc7b904aa66c84aed9e4ab99772cb11da2ccc3a6d51fb6bc9719aed73c
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