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 tokenizer.json from gitmodelmujtaba/sapbert-snomed-loinc-rxnorm: direct link, hf CLI and curl.
- Browser
- Download file 706 kB
-
https://huggingface.co/gitmodelmujtaba/sapbert-snomed-loinc-rxnorm/resolve/main/tokenizer.json
- Command line
-
hf download hf://gitmodelmujtaba/sapbert-snomed-loinc-rxnorm/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/gitmodelmujtaba/sapbert-snomed-loinc-rxnorm/resolve/main/tokenizer.json
706 kB
File too large to display, you can check the raw version instead.