Sentence Similarity
sentence-transformers
Safetensors
Transformers
Russian
bert
pretraining
russian
embeddings
tiny
feature-extraction
text-embeddings-inference
Instructions to use sergeyzh/rubert-tiny-sts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sergeyzh/rubert-tiny-sts with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sergeyzh/rubert-tiny-sts") sentences = [ "Это счастливый человек", "Это счастливая собака", "Это очень счастливый человек", "Сегодня солнечный день" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use sergeyzh/rubert-tiny-sts with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("sergeyzh/rubert-tiny-sts") model = AutoModelForPreTraining.from_pretrained("sergeyzh/rubert-tiny-sts", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d8f19204628622afb559a138df4b21af9a1259224f104ac529fccc6d7feaf742
- Size of remote file:
- 118 MB
- SHA256:
- 69b3d1d73b5d33017d8d4dccbe84e960cff71a0c48684911624eae680839e381
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.