Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
dense
Generated from Trainer
dataset_size:76932
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use blemond/RAG_press_multilingual_e5_large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use blemond/RAG_press_multilingual_e5_large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("blemond/RAG_press_multilingual_e5_large") sentences = [ "query: ATM Adaptation Layer 2의 약어는 무엇인가요?", "passage: 2 Transmit 2 Receive (기술)", "passage: Alternating Current (개념)", "passage: AAL2 (기술)" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Ctrl+K