Sentence Similarity
sentence-transformers
Safetensors
Slovak
xlm-roberta
feature-extraction
dense
Generated from Trainer
dataset_size:137745
loss:CosineSimilarityLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use borsimnet/e5-sk-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use borsimnet/e5-sk-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("borsimnet/e5-sk-large") sentences = [ "Mor a epidémia sa očividne vymkli spod kontroly .", "Choroba bola nekontrolovateľná a ohrozovala všetok život .", "Tieto vylúčenia sú určené na iné cieľové skupiny ako obchodné štvrte .", "Autobus National Trust Tour odchádza každý deň o 9:00 z National Trust Information Centre ." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from borsimnet/e5-sk-large: direct link, hf CLI and curl.
- Browser
- Download file 4.16 MB
-
https://huggingface.co/borsimnet/e5-sk-large/resolve/main/tokenizer.json
- Command line
-
hf download hf://borsimnet/e5-sk-large/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/borsimnet/e5-sk-large/resolve/main/tokenizer.json
4.16 MB
File too large to display, you can check the raw version instead.