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: 277 Bytes
3d68cb6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | [
{
"idx": 0,
"name": "0",
"path": "",
"type": "sentence_transformers.base.modules.transformer.Transformer"
},
{
"idx": 1,
"name": "1",
"path": "1_Pooling",
"type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
}
] |