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
| { | |
| "transformer_task": "feature-extraction", | |
| "modality_config": { | |
| "text": { | |
| "method": "forward", | |
| "method_output_name": "last_hidden_state" | |
| } | |
| }, | |
| "module_output_name": "token_embeddings" | |
| } |