Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
dense
Generated from Trainer
dataset_size:43318
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use leewonjun/e5-mul-0910a with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use leewonjun/e5-mul-0910a with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("leewonjun/e5-mul-0910a") sentences = [ "query: 3PL 사용 시의 비용 절감 메커니즘은 어떤 것이 있나요?", "passage: 3 Dimension-Through Silicon Via (Technical)", "passage: Third Party Logistics (상업)", "passage: Authorization Account Answer (Technical)" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Ctrl+K