Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
feature-extraction
Generated from Trainer
dataset_size:1567
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Mdean77/modernbert-embed-quickb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Mdean77/modernbert-embed-quickb with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Mdean77/modernbert-embed-quickb") sentences = [ "How many authors are listed for the trial?", "chemotherapy and bone marrow transplantation for certain malignancies and has a long track\nrecord of safe use in adults and children. The incidence of adverse events such as fever, chills,\nbone pain, dyspnea, tachycardia, and hemodynamic instability was no different between GM-\nCSF and placebo-treated groups in controlled adult BMT studies. Rapid IV administration of", "clinical ICU staff in accordance with institutional practice and judgment.\nChild Assent Subjects who are eligible for this study will be critically ill, and child assent is\ntypically not possible at the time of study enrollment. However, during follow up after discharge\nfrom the ICU, issues about assent become applicable. Children who are capable of giving assent", "Controlled Phase 2 Trial. Stroke, 49(5):1210–1216, 2018.\n[76] M. K. R. Somagutta, M. K. Lourdes Pormento, P. Hamid, A. Hamdan, M. A. Khan,\nR. Desir, R. Vijayan, S. Shirke, R. Jeyakumar, Z. Dogar, S. S. Makkar, P. Guntipalli,\nN. N. Ngardig, M. S. Nagineni, T. Paul, E. Luvsannyam, C. Riddick, and M. A. Sanchez-" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Welcome to the community
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