Sentence Similarity
sentence-transformers
Safetensors
English
feature-extraction
Generated from Trainer
dataset_size:4373977
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
Instructions to use LocalWisdom/PurpleStatic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LocalWisdom/PurpleStatic with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LocalWisdom/PurpleStatic") sentences = [ "crack", "master", "Academic journals often publish both print editions and digital versions simultaneously.", "ceramic ware" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9228721e4e5e84a28e076b5ed1c7f58d8739a512e8026317a3475f4beb847bec
- Size of remote file:
- 411 MB
- SHA256:
- 10780338fbc2ed9c49998b01a7d7b5276a36a5852adebf9fa92320c50174b1b4
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