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
- Xet hash:
- 9b24e182fc9f11dd1a75c36cefadfe3ae05f13569282622cc2a26ec1a961d3a7
- Size of remote file:
- 5.5 kB
- SHA256:
- 1ea9dc835ea4084259faed880db5dc5ca6dc68cbebd09d04f8f2b4b4907bce1a
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