Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
feature-extraction
Generated from Trainer
dataset_size:321
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use densonsmith/modernbert-embed-quickb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use densonsmith/modernbert-embed-quickb with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("densonsmith/modernbert-embed-quickb") sentences = [ "Since what year have they been married?", "Graph: Team Coco Knowledge Graph\nNode ID: 2015_conan_cuba\nCategory: events\nName: Conan in Cuba\nType: Event\n\nDescription: Conan O'Brien traveled to Havana to film a historic episode—the first by an American late-night host in over 50 years—part of his 'Conan Without Borders' specials.\n\nRelationships:\n- Host conan_obrien\n- Occurred during conan_tbs", "Description: Liza Powel O'Brien is an American playwright and podcast host. She met Conan O'Brien in 2000 while working at an advertising agency, and they married in 2002. She has written numerous plays staged at theaters like the Geffen Playhouse and Ojai Playwrights Conference, and in 2022 she launched the history podcast \"Significant Others\" on Conan's Team Coco network.", "Relationships:\n- Spouse conan_obrien (Strength: very strong)\n Description: Married since 2002; they have two children together.\n- Podcast host team_coco (Strength: moderate)\n Description: Hosts the \"Significant Others\" podcast under the Team Coco banner." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle