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
File size: 440 Bytes
3d68cb6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"__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"
} |