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
File size: 190 Bytes
8a62069 | 1 2 3 4 5 6 7 | {
"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
} |