Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
text-embeddings-inference
Instructions to use NLBSE/nlbse25_java with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use NLBSE/nlbse25_java with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("NLBSE/nlbse25_java") - sentence-transformers
How to use NLBSE/nlbse25_java with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NLBSE/nlbse25_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
File size: 296 Bytes
bce76ea | 1 2 3 4 5 6 7 8 9 10 | {
"word_embedding_dimension": 384,
"pooling_mode_cls_token": false,
"pooling_mode_mean_tokens": true,
"pooling_mode_max_tokens": false,
"pooling_mode_mean_sqrt_len_tokens": false,
"pooling_mode_weightedmean_tokens": false,
"pooling_mode_lasttoken": false,
"include_prompt": true
} |