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
File size: 462 Bytes
23e04b5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"optimizer": "PairAdam",
"stsb_dev_spearman_before": 0.4551,
"stsb_dev_spearman_after": 0.7179,
"stsb_test_spearman_before": 0.2849,
"stsb_test_spearman_after": 0.6242,
"stsb_test_pearson_before": 0.2981,
"stsb_test_pearson_after": 0.6330,
"adamw_ablation": {
"stsb_dev_spearman_after": 0.7289,
"stsb_test_spearman_after": 0.6317,
"stsb_test_pearson_after": 0.6372
},
"parameters": 29305497,
"trainable_parameters": 9891865
}
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