Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
text-embeddings-inference
Instructions to use ThomBors/NLBSE2026-python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use ThomBors/NLBSE2026-python with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("ThomBors/NLBSE2026-python") - sentence-transformers
How to use ThomBors/NLBSE2026-python with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ThomBors/NLBSE2026-python") 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-python: direct link, hf CLI and curl.
- Browser
- Download file 18 kB
-
https://huggingface.co/ThomBors/NLBSE2026-python/resolve/main/model_head.pkl
- Command line
-
hf download hf://ThomBors/NLBSE2026-python/model_head.pkl
-
curl -L -o model_head.pkl https://huggingface.co/ThomBors/NLBSE2026-python/resolve/main/model_head.pkl
18 kB
- Xet hash:
- b9d084719879c6b877844df0cf2489b240fd3efece34d8c9fcc6138401a2cf39
- Size of remote file:
- 18 kB
- SHA256:
- fc680a095219e5e984d397835e8339bbeeb0a02b82007567af15d0e8e9dbbc62
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.