Sentence Similarity
sentence-transformers
Safetensors
roberta
feature-extraction
dense
Generated from Trainer
dataset_size:137745
loss:CosineSimilarityLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use mrshu/sturovec-base-sk-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mrshu/sturovec-base-sk-v0 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mrshu/sturovec-base-sk-v0") sentences = [ "Napriek tomu , myšlienka v liste dnešným Times od profesora medicíny z Yale je dobrá , stačí dať policajta na Springerov set .", "List bol od profesora , ktorý vyučuje fyziológiu a anatómiu na Yale .", "Whittington by rozpoznal herečku bez akéhokoľvek make-upu .", "Bushova rodina poskytuje vynikajúce rozptýlenie ." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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