Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
text-embeddings-inference
Instructions to use ThomBors/NLBSE2026-java with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use ThomBors/NLBSE2026-java with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("ThomBors/NLBSE2026-java") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - sentence-transformers
How to use ThomBors/NLBSE2026-java with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ThomBors/NLBSE2026-java") 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] - Notebooks
- Google Colab
- Kaggle
Download checkpoint-48015/optimizer.pt from ThomBors/NLBSE2026-java: direct link, hf CLI and curl.
- Browser
- Download file 181 MB
-
https://huggingface.co/ThomBors/NLBSE2026-java/resolve/main/checkpoint-48015/optimizer.pt
- Command line
-
hf download hf://ThomBors/NLBSE2026-java/checkpoint-48015/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/ThomBors/NLBSE2026-java/resolve/main/checkpoint-48015/optimizer.pt
181 MB
- Xet hash:
- 5dd568037ed80bb8b68f3e8d516f14490a551eca5c36f386ae45fac5e6a7eebe
- Size of remote file:
- 181 MB
- SHA256:
- 76eb87cae5a17784e0f901f841fe782b6a7361ee0d9e870063f75152e2bfe2a2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.