Sentence Similarity
sentence-transformers
Safetensors
roberta
feature-extraction
Generated from Trainer
dataset_size:19383
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use nanalysenko/panacea_v2.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nanalysenko/panacea_v2.2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nanalysenko/panacea_v2.2") sentences = [ "12.02.2.17 Панель ингаляционных аллергенов № 9 (IgE): эпителий кошки, перхоть собаки, овсяница луговая", "Панель аллергенов плесени № 1 IgE (penicillium notatum, cladosporium herbarum, aspergillus fumigatus, candida albicans, alternaria tenuis),", "Панель пищевых аллергенов № 51 IgE (помидор, картофель, морковь, чеснок, горчица),", "Прием (осмотр, консультация) врача-психотерапевта первичный" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |