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 1_Pooling/config.json from WpythonW/rubert-tiny2-vllm: direct link, hf CLI and curl.
- Browser
- Download file 190 Bytes
-
https://huggingface.co/WpythonW/rubert-tiny2-vllm/resolve/22e0853ebfd8ea3c84c672274bf6501da27040c4/1_Pooling/config.json
- Command line
-
hf download hf://WpythonW/rubert-tiny2-vllm@22e0853ebfd8ea3c84c672274bf6501da27040c4/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/WpythonW/rubert-tiny2-vllm/resolve/22e0853ebfd8ea3c84c672274bf6501da27040c4/1_Pooling/config.json
190 Bytes
| { | |
| "word_embedding_dimension": 312, | |
| "pooling_mode_cls_token": true, | |
| "pooling_mode_mean_tokens": false, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false | |
| } |