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
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c987cec1b71deba4a1749a939032309921872ae2a6c21378f8160edb29b210df config.json
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b15e9a2b676eeb6b5ed466de9be3edf23422cf8ca005fde5e04aef05bc032906 config_sentence_transformers.json
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3bb3b9830c9d0d05e0950fc457bdedcca0f4592ad1825f4d6392aa84dc5d0093 GRT_Whitepaper.pdf
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