Sentence Similarity
sentence-transformers
Safetensors
feature-extraction
Generated from Trainer
dataset_size:11172
loss:MultipleNegativesRankingLoss
custom_code
Eval Results (legacy)
Instructions to use wwydmanski/cde-small-pubmed-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use wwydmanski/cde-small-pubmed-v0.1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("wwydmanski/cde-small-pubmed-v0.1", trust_remote_code=True) sentences = [ "Carditis in pediatric patients following foreign serum administration", "Four cases of carditis occurring in children and associated with the administration of a foreign serum. ", "Understanding Positive Youth Development in Sport Through the Voices of Indigenous Youth. ", "Pericarditis in children. " ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 13fc4fd120d440430fbf8c03abd1e48df9c7d7d0228c06e9ea6f6dfa2b6d7230
- Size of remote file:
- 1.12 GB
- SHA256:
- bc2f0a8481c3c39aafaf1904441c98ca35bfaddcf14522da8434187e50d7621e
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