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") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from BSC-NLP4BIA/location-sub-classifier: direct link, hf CLI and curl.
- Browser
- Download file 504 MB
-
https://huggingface.co/BSC-NLP4BIA/location-sub-classifier/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://BSC-NLP4BIA/location-sub-classifier@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/BSC-NLP4BIA/location-sub-classifier/resolve/refs%2Fpr%2F1/pytorch_model.bin
504 MB
- Xet hash:
- 6ade469c60d5608fc7f429a0875db5bc373bca50885aa365e94e89304c93a0f5
- Size of remote file:
- 504 MB
- SHA256:
- 039e494a499b4aef17e52cc2d4369ac0e927088bcc8f4738c7183c6959be9d7d
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