Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:198
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use luka023/proba with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use luka023/proba with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("luka023/proba") sentences = [ "Najčešći tipovi uključuju iznad/ispod 2.5, ukupno golova, i klađenje na broj golova u poluvremenima.", "Koji su najčešći tipovi klađenja na golove?", "Koje kladionice u Srbiji nude DNB opciju?", "Šta je hendikep klađenje?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- db4bd8239447f64f68d10753125b60c051d0114d95f9df472946c33fb5201843
- Size of remote file:
- 2.24 GB
- SHA256:
- 0d415029e4b7550875431ce9c454dda2cf594d2205a6c44fabf062903ac23fb3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.