Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
dense
Generated from Trainer
dataset_size:248978
loss:OnlineContrastiveLoss
text-embeddings-inference
Instructions to use pa-shk/USER-bge-m3_cats with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use pa-shk/USER-bge-m3_cats with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("pa-shk/USER-bge-m3_cats") sentences = [ "Продукт Lami Гхи из топлёного сливочного масла и кокосового масла, саше", "Чехол для клавиатуры", "Батарейка Крона D", "Тан" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Ctrl+K