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
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:ade741d7575f89fe65b42e655cacf6585bdb66cad1a51f1dcae50056ac98173c
|
| 3 |
+
size 503934824
|