Sentence Similarity
sentence-transformers
Safetensors
Russian
modernbert
feature-extraction
code-retrieval
1c
bsl
matryoshka
Eval Results (legacy)
text-embeddings-inference
Instructions to use PruhaNLP/USER2-1C-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use PruhaNLP/USER2-1C-code with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("PruhaNLP/USER2-1C-code") 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
| { | |
| "__version__": { | |
| "pytorch": "2.8.0+cu128", | |
| "sentence_transformers": "5.5.1", | |
| "transformers": "5.1.0" | |
| }, | |
| "default_prompt_name": null, | |
| "model_type": "SentenceTransformer", | |
| "prompts": { | |
| "classification": "classification: ", | |
| "clustering": "clustering: ", | |
| "document": "", | |
| "query": "", | |
| "search_document": "search_document: ", | |
| "search_query": "search_query: " | |
| }, | |
| "similarity_fn_name": "cosine" | |
| } |