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 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Shakhovak/tiny_sent_transformer with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Shakhovak/tiny_sent_transformer") sentences = [ "Тренажер на свободных весах DFC HOMEGYM HM019 в Москве", "Независимая бицепс-машина Matrix G7-S40", "Беговая дорожка Stingrey ST-9317", "Мультикомплекс Hasttings Digger HD003-7" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download sentence_bert_config.json from Shakhovak/tiny_sent_transformer: direct link, hf CLI and curl.
- Browser
- Download file 57 Bytes
-
https://huggingface.co/Shakhovak/tiny_sent_transformer/resolve/main/sentence_bert_config.json
- Command line
-
hf download hf://Shakhovak/tiny_sent_transformer/sentence_bert_config.json
-
curl -L -o sentence_bert_config.json https://huggingface.co/Shakhovak/tiny_sent_transformer/resolve/main/sentence_bert_config.json
57 Bytes
| { | |
| "max_seq_length": 2048, | |
| "do_lower_case": false | |
| } |