Sentence Similarity
sentence-transformers
PyTorch
xlm-roberta
feature-extraction
dense
Generated from Trainer
dataset_size:747
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use cringeee/be_finetuned_mnr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cringeee/be_finetuned_mnr with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cringeee/be_finetuned_mnr") sentences = [ "шанобливо вклонятися, виражаючи почуття поваги", "шанований, відомий своїм становищем в суспільстві; в пошані", "для вираження поваги, пошани кому-, чому-небудь, для підтвердження визнання когось, чогось", "не мати достатнього життєвого досвіду" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from cringeee/be_finetuned_mnr: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/cringeee/be_finetuned_mnr/resolve/main/tokenizer.json
- Command line
-
hf download hf://cringeee/be_finetuned_mnr/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/cringeee/be_finetuned_mnr/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- c9abfbcdb4ffc00d355a5aceeb3df3cad5ed9f079223b9434115e96b6423ffb1
- Size of remote file:
- 17.1 MB
- SHA256:
- cad551d5600a84242d0973327029452a1e3672ba6313c2a3c3d69c4310e12719
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