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
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widget:
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- source_sentence: crack
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sentences:
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- loss:MatryoshkaLoss
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datasets:
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- BatuhanECB/FinModernBERT-pairs-sec-synthetic-v1
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- Heliosoph/Quora-Question-Pairs
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- owenkaplinsky/wildchat-paraphrases
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- mjbommar/ogbert-v1-contrastive
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- mjbommar/opengloss-v1.3-contrastive-examples
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widget:
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- source_sentence: crack
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sentences:
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