Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:90624
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use OwlCHN/kbqa_retriever_dev_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use OwlCHN/kbqa_retriever_dev_v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("OwlCHN/kbqa_retriever_dev_v1") sentences = [ "fetch relations: what are the risks of a medical condition which may prevent pyrazinamide?", "medicine.risk_factor.diseases", "architecture.lighthouse.construction", "zoos.zoo" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Ctrl+K