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
Download tokenizer.json from laura2243/deberta-sota: direct link, hf CLI and curl.
- Browser
- Download file 8.65 MB
-
https://huggingface.co/laura2243/deberta-sota/resolve/main/tokenizer.json
- Command line
-
hf download hf://laura2243/deberta-sota/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/laura2243/deberta-sota/resolve/main/tokenizer.json
8.65 MB
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