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 model_head.pkl from ThomBors/NLBSE2026-java: direct link, hf CLI and curl.
- Browser
- Download file 945 kB
-
https://huggingface.co/ThomBors/NLBSE2026-java/resolve/main/model_head.pkl
- Command line
-
hf download hf://ThomBors/NLBSE2026-java/model_head.pkl
-
curl -L -o model_head.pkl https://huggingface.co/ThomBors/NLBSE2026-java/resolve/main/model_head.pkl
945 kB
- Xet hash:
- db8f8ea5ed1f935c1d7bc1467e4d3e7c16e8fcbde8d23556922664bf029b70c1
- Size of remote file:
- 945 kB
- SHA256:
- 264fc758e33b2f8c1d2bc6ce6226c23a353658b78d5f0fac098cfb210ab6e376
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