Feature Extraction
Transformers
Safetensors
English
bert
medical
clinical
sapbert
biomedical
snomed-ct
loinc
rxnorm
omop
metric-learning
entity-linking
cross-encoder
graph-rag
knowledge-graph
text-embeddings-inference
Instructions to use gitmodelmujtaba/sapbert-snomed-loinc-rxnorm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gitmodelmujtaba/sapbert-snomed-loinc-rxnorm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="gitmodelmujtaba/sapbert-snomed-loinc-rxnorm")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("gitmodelmujtaba/sapbert-snomed-loinc-rxnorm") model = AutoModel.from_pretrained("gitmodelmujtaba/sapbert-snomed-loinc-rxnorm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download cross_encoder/pytorch_model.bin from gitmodelmujtaba/sapbert-snomed-loinc-rxnorm: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/gitmodelmujtaba/sapbert-snomed-loinc-rxnorm/resolve/main/cross_encoder/pytorch_model.bin
- Command line
-
hf download hf://gitmodelmujtaba/sapbert-snomed-loinc-rxnorm/cross_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/gitmodelmujtaba/sapbert-snomed-loinc-rxnorm/resolve/main/cross_encoder/pytorch_model.bin
438 MB
- Xet hash:
- 7613bb1f3e0deb689ce560d79e64de4420c59303c598fe4f5f90c7804bd834bc
- Size of remote file:
- 438 MB
- SHA256:
- 5fa0444757399fd6688cc5bad4be6bc2303a0680e38955f6a6c5402f5ad1c0f7
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