Sentence Similarity
sentence-transformers
Safetensors
deberta-v2
feature-extraction
dense
Generated from Trainer
dataset_size:44114
loss:ContrastiveLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use laura2243/deberta-sota with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use laura2243/deberta-sota with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("laura2243/deberta-sota") sentences = [ "The Sadrist movement left the Alliance before the elections in December 2005 , which also brought the Iraqi National Congress more firmly to the Alliance .", "The Iraqi National Congress left the Alliance before the December 2005 elections , which also brought the Sadrist movement more to the Alliance .", "He pioneered important developments in the style of sculpting in wood , parallel to those driven by Filippo Parodi in marble sculpture and Domenico Piola in painting .", "The Mine South Deep is a large mine in the northern part of Gauteng in South Africa ." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_type": "SentenceTransformer", | |
| "__version__": { | |
| "sentence_transformers": "5.2.0", | |
| "transformers": "4.57.3", | |
| "pytorch": "2.9.0+cu126" | |
| }, | |
| "prompts": { | |
| "query": "", | |
| "document": "" | |
| }, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": "cosine" | |
| } |