Text Classification
setfit
Safetensors
sentence-transformers
qwen3
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use fefofico/nuclear_trained_f2llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use fefofico/nuclear_trained_f2llm with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("fefofico/nuclear_trained_f2llm") - sentence-transformers
How to use fefofico/nuclear_trained_f2llm with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("fefofico/nuclear_trained_f2llm") 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
- Xet hash:
- ba0edca9db8be76fb776feccc4fd7d197fe011149991dd935a0f0ffa0a98c2b4
- Size of remote file:
- 2.14 kB
- SHA256:
- 86c15b221bfb9a5e4893c9ab2885cd1964e8d01eae4b677a8ecfcec54685d243
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.