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)
Browse files- eval_results.json +6 -0
eval_results.json
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"stsb_test_spearman_after": 0.6242,
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"stsb_test_pearson_before": 0.2981,
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"stsb_test_pearson_after": 0.6330,
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"adamw_ablation": {
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"stsb_dev_spearman_after": 0.7289,
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"stsb_test_spearman_after": 0.6317,
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"stsb_test_spearman_after": 0.6242,
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"stsb_test_pearson_before": 0.2981,
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"stsb_test_pearson_after": 0.6330,
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"hessian_pair_adam_ablation": {
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"stsb_dev_spearman_after": 0.7278,
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"stsb_dev_pearson_after": 0.6917,
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"stsb_test_spearman_after": 0.6288,
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"stsb_test_pearson_after": 0.6316
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},
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"adamw_ablation": {
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"stsb_dev_spearman_after": 0.7289,
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"stsb_test_spearman_after": 0.6317,
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