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 SHA256SUMS with 16-way Classical Geometric, Symplectic & Toroidal optimizer ablation
Browse files- SHA256SUMS +3 -3
SHA256SUMS
CHANGED
|
@@ -1,13 +1,13 @@
|
|
| 1 |
a4a8edb13245bb69ccb55a2a2c3f322221e2a16154c96516c9211274030bf815 1_Pooling/config.json
|
| 2 |
c987cec1b71deba4a1749a939032309921872ae2a6c21378f8160edb29b210df config.json
|
| 3 |
b15e9a2b676eeb6b5ed466de9be3edf23422cf8ca005fde5e04aef05bc032906 config_sentence_transformers.json
|
| 4 |
-
|
| 5 |
79219257df919cc598a75f7ae0968988ed0d33ffc1777469f9a9da39a558819d GRT_Whitepaper.pdf
|
| 6 |
5cdab387c5e2ee9bb3907acaccfccfaa7cc10060c21e3f265ce981a4a7d38226 model.safetensors
|
| 7 |
e86db55b23831f6efca3d43309863db7d3b96ec21fbf42cffa5afc35fdcc61af modules.json
|
| 8 |
-
|
| 9 |
48fd1319867cb7251a5568790b0bf95b4e027609630180d9097c86b0d974b3dc PROVENANCE.md
|
| 10 |
-
|
| 11 |
b344933f060b173208ed2f9b7a35c1cbf7b1a22b53cba09735bfbdbff5b04848 s1-grt-tokenizer-cache-76e9e7b2014f0479.pkl
|
| 12 |
f40b497174aa416490cf8c6e70ef19ad68f2d20160de9ca1fa8a130e89b2ee19 sentence_bert_config.json
|
| 13 |
76e9e7b2014f0479052f15dfe092fc5dfadfb52b57b8133a141a4d0deb89a6f7 tokenizer.json
|
|
|
|
| 1 |
a4a8edb13245bb69ccb55a2a2c3f322221e2a16154c96516c9211274030bf815 1_Pooling/config.json
|
| 2 |
c987cec1b71deba4a1749a939032309921872ae2a6c21378f8160edb29b210df config.json
|
| 3 |
b15e9a2b676eeb6b5ed466de9be3edf23422cf8ca005fde5e04aef05bc032906 config_sentence_transformers.json
|
| 4 |
+
8b86cdf6e3d07171b07c8e1556a9c92e2c673aa5be6212999683531d47aa9a5a eval_results.json
|
| 5 |
79219257df919cc598a75f7ae0968988ed0d33ffc1777469f9a9da39a558819d GRT_Whitepaper.pdf
|
| 6 |
5cdab387c5e2ee9bb3907acaccfccfaa7cc10060c21e3f265ce981a4a7d38226 model.safetensors
|
| 7 |
e86db55b23831f6efca3d43309863db7d3b96ec21fbf42cffa5afc35fdcc61af modules.json
|
| 8 |
+
2e73566568df5e2bd595f8805a5f9da2bfc859c0888bf15512ef4bef579696d7 optimizer_ablation_results.json
|
| 9 |
48fd1319867cb7251a5568790b0bf95b4e027609630180d9097c86b0d974b3dc PROVENANCE.md
|
| 10 |
+
606a9522e8497a169bfc5c7ed58f43799a67dde7a2e456276427736419f21630 README.md
|
| 11 |
b344933f060b173208ed2f9b7a35c1cbf7b1a22b53cba09735bfbdbff5b04848 s1-grt-tokenizer-cache-76e9e7b2014f0479.pkl
|
| 12 |
f40b497174aa416490cf8c6e70ef19ad68f2d20160de9ca1fa8a130e89b2ee19 sentence_bert_config.json
|
| 13 |
76e9e7b2014f0479052f15dfe092fc5dfadfb52b57b8133a141a4d0deb89a6f7 tokenizer.json
|