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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Add 3-way STS-B optimizer ablation (PairAdam vs HessianPairAdam Sophia-Torus vs AdamW)
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Update README: Apache 2.0 and matterwave.ai citation
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2 days ago
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Update README: remove GNU Free Software Statement, keep Apache 2.0 and matterwave.ai citation
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Update README with GRT-30M suite, GRT_Whitepaper.pdf, GNU Free-as-in-Freedom statement, and matterwave.ai citation
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Update README with GRT-30M suite, GRT_Whitepaper.pdf, GNU Free-as-in-Freedom statement, and matterwave.ai citation
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Add GRT Whitepaper links, GNU Free-as-in-Freedom statement, and matterwave.ai citation
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Release PairAdam-trained GRT-30M-Embedding checkpoint with AdamW ablation note
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docs: document two-stage contrastive fine-tuning method on Intel Arc XPU
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Release MatterWaveAI/grt-30m-embedding
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