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 config_sentence_transformers.json from deepvk/USER2-base: direct link, hf CLI and curl.
- Browser
- Download file 359 Bytes
-
https://huggingface.co/deepvk/USER2-base/resolve/main/config_sentence_transformers.json
- Command line
-
hf download hf://deepvk/USER2-base/config_sentence_transformers.json
-
curl -L -o config_sentence_transformers.json https://huggingface.co/deepvk/USER2-base/resolve/main/config_sentence_transformers.json
359 Bytes
| { | |
| "__version__": { | |
| "sentence_transformers": "4.0.2", | |
| "transformers": "4.49.0", | |
| "pytorch": "2.6.0" | |
| }, | |
| "prompts": { | |
| "classification": "classification: ", | |
| "clustering": "clustering: ", | |
| "search_query": "search_query: ", | |
| "search_document": "search_document: " | |
| }, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": "cosine" | |
| } |