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
Update 17-way classical geometric optimizer ablation (add HuygensSymplecticAdam): optimizer_ablation_results.json
Browse files
optimizer_ablation_results.json
CHANGED
|
@@ -110,5 +110,14 @@
|
|
| 110 |
"stsb_test_spearman_after": 0.6264,
|
| 111 |
"stsb_test_pearson_after": 0.6353,
|
| 112 |
"elapsed_sec": 271.68
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 113 |
}
|
| 114 |
}
|
|
|
|
| 110 |
"stsb_test_spearman_after": 0.6264,
|
| 111 |
"stsb_test_pearson_after": 0.6353,
|
| 112 |
"elapsed_sec": 271.68
|
| 113 |
+
},
|
| 114 |
+
"huygens_symplectic_adam": {
|
| 115 |
+
"variant": "HuygensSymplecticAdam (Huygens Splay Curriculum -> Symplectic Non-Squeezing + Casimir Cavity)",
|
| 116 |
+
"s1_dev_spearman": 0.6282,
|
| 117 |
+
"stsb_dev_spearman_after": 0.7368,
|
| 118 |
+
"stsb_dev_pearson_after": 0.709,
|
| 119 |
+
"stsb_test_spearman_after": 0.6509,
|
| 120 |
+
"stsb_test_pearson_after": 0.6548,
|
| 121 |
+
"elapsed_sec": 236.41
|
| 122 |
}
|
| 123 |
}
|