Sentence Similarity
sentence-transformers
Safetensors
Russian
English
bert
embeddings
vllm
inference-optimized
inference
text-embeddings-inference
Instructions to use WpythonW/rubert-tiny2-vllm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use WpythonW/rubert-tiny2-vllm with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("WpythonW/rubert-tiny2-vllm") 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] - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from WpythonW/rubert-tiny2-vllm: direct link, hf CLI and curl.
- Browser
- Download file 116 MB
-
https://huggingface.co/WpythonW/rubert-tiny2-vllm/resolve/main/model.safetensors
- Command line
-
hf download hf://WpythonW/rubert-tiny2-vllm/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/WpythonW/rubert-tiny2-vllm/resolve/main/model.safetensors
116 MB
- Xet hash:
- 93ee5ec6f3d45b679dd863415ef7a5b0be23c6833a802bd25ff631e90f69e57e
- Size of remote file:
- 116 MB
- SHA256:
- 6cfc4e638c4147af426614dcbc399ee3c7e3ff7026992dbbd3a1b0bed414c0cd
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