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
Add 3-way STS-B optimizer ablation (PairAdam vs HessianPairAdam Sophia-Torus vs AdamW)
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79219257df919cc598a75f7ae0968988ed0d33ffc1777469f9a9da39a558819d GRT_Whitepaper.pdf
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b344933f060b173208ed2f9b7a35c1cbf7b1a22b53cba09735bfbdbff5b04848 s1-grt-tokenizer-cache-76e9e7b2014f0479.pkl
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f40b497174aa416490cf8c6e70ef19ad68f2d20160de9ca1fa8a130e89b2ee19 sentence_bert_config.json
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a4a8edb13245bb69ccb55a2a2c3f322221e2a16154c96516c9211274030bf815 1_Pooling/config.json
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c987cec1b71deba4a1749a939032309921872ae2a6c21378f8160edb29b210df config.json
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b15e9a2b676eeb6b5ed466de9be3edf23422cf8ca005fde5e04aef05bc032906 config_sentence_transformers.json
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dd4426f9d76e44e08b541e480bc0985897edd57f80bbb7682f4393ed0e4769ba eval_results.json
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79219257df919cc598a75f7ae0968988ed0d33ffc1777469f9a9da39a558819d GRT_Whitepaper.pdf
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5cdab387c5e2ee9bb3907acaccfccfaa7cc10060c21e3f265ce981a4a7d38226 model.safetensors
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48fd1319867cb7251a5568790b0bf95b4e027609630180d9097c86b0d974b3dc PROVENANCE.md
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14490e255b07b379c9886b1470e241aea889cb79ff13ad4f33a7efa878420e5a README.md
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b344933f060b173208ed2f9b7a35c1cbf7b1a22b53cba09735bfbdbff5b04848 s1-grt-tokenizer-cache-76e9e7b2014f0479.pkl
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76e9e7b2014f0479052f15dfe092fc5dfadfb52b57b8133a141a4d0deb89a6f7 tokenizer.json
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