Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:13690
loss:ContrastiveLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Shakhovak/tiny_sent_transformer_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Shakhovak/tiny_sent_transformer_v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Shakhovak/tiny_sent_transformer_v2") sentences = [ "Грузоблочный тренажер Bronze Gym D-015 - жим ногами в Москве Силовые тренажеры Грузоблочные Bronze Gym D-015 - жим ногами", "Трицепс-машина Matrix G3-S45 Главная Силовые тренажеры Трицепс-машина Matrix G3-S45", "Верхняя тяга Iron Bull IR-TE08 nan Силовые тренажеры Грузоблочные тренажеры", "Горизонтальный велоэргометр Matrix Lifestyle с консолью LED nan Велотренажеры Matrix" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Shakhovak/tiny_sent_transformer_v2: direct link, hf CLI and curl.
- Browser
- Download file 2.41 MB
-
https://huggingface.co/Shakhovak/tiny_sent_transformer_v2/resolve/main/tokenizer.json
- Command line
-
hf download hf://Shakhovak/tiny_sent_transformer_v2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Shakhovak/tiny_sent_transformer_v2/resolve/main/tokenizer.json
2.41 MB
File too large to display, you can check the raw version instead.