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 1_Pooling/config.json from cringeee/be_finetuned_mnr: direct link, hf CLI and curl.
- Browser
- Download file 321 Bytes
-
https://huggingface.co/cringeee/be_finetuned_mnr/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://cringeee/be_finetuned_mnr/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/cringeee/be_finetuned_mnr/resolve/main/1_Pooling/config.json
321 Bytes
| { | |
| "word_embedding_dimension": 768, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |