Text Classification
sentence-transformers
PyTorch
setfit
Spanish
roberta
biomedical
clinical
EHR
spanish
location
birth place
residence
movement
medical care
Eval Results (legacy)
text-embeddings-inference
Instructions to use BSC-NLP4BIA/location-sub-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BSC-NLP4BIA/location-sub-classifier with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BSC-NLP4BIA/location-sub-classifier") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - setfit
How to use BSC-NLP4BIA/location-sub-classifier with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("BSC-NLP4BIA/location-sub-classifier") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1 opened over 1 year ago
by
SFconvertbot