Sentence Similarity
sentence-transformers
Safetensors
English
grt
geometric-rotation-transformer
feature-extraction
circular-pooling
rotational
qwen3
Instructions to use MatterWaveAI/grt-30m-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use MatterWaveAI/grt-30m-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("MatterWaveAI/grt-30m-embedding") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Commit History
Yesterday
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Update 17-way classical geometric optimizer ablation (add HuygensSymplecticAdam): eval_results.json
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Update eval_results.json with 16-way Classical Geometric, Symplectic & Toroidal optimizer ablation
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2 days ago
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Update eval_results.json with 9-way CyclideAdam & Toroidal optimizer ablation
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3 days ago
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Add complete 8-way toroidal & Riemannian optimizer ablation (PolarNaturalAdam 64.71 Test, ToroidalMuon 73.61 Val, RotorAdam, ToroidalLion)
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4 days ago
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Add 3-way STS-B optimizer ablation (PairAdam vs HessianPairAdam Sophia-Torus vs AdamW)
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5 days ago
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Release PairAdam-trained GRT-30M-Embedding checkpoint with AdamW ablation note
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Release MatterWaveAI/grt-30m-embedding
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