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
File size: 383 Bytes
2033599 | 1 2 3 4 | epoch,steps,cosine_accuracy,cosine_accuracy_threshold,cosine_f1,cosine_precision,cosine_recall,cosine_f1_threshold,cosine_ap,cosine_mcc
1.0,2758,0.8955465587044534,0.8826057453064917,0.8717605004468275,0.8937242327072835,0.936077357984464,0.787759670906764
2.0,5516,0.9121457489878543,0.9024280575539567,0.8860927152317881,0.9193770041227668,0.9503471324249102,0.8230430822451054
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