Sentence Similarity
sentence-transformers
ONNX
Safetensors
Russian
modernbert
feature-extraction
text-embeddings-inference
Instructions to use deepvk/USER2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use deepvk/USER2-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("deepvk/USER2-base") 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] - Inference
- Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from deepvk/USER2-base: direct link, hf CLI and curl.
- Browser
- Download file 4.75 MB
-
https://huggingface.co/deepvk/USER2-base/resolve/main/tokenizer.json
- Command line
-
hf download hf://deepvk/USER2-base/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/deepvk/USER2-base/resolve/main/tokenizer.json
4.75 MB
File too large to display, you can check the raw version instead.