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): SHA256SUMS
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 |
+
631eb037b9a77a9bec98adc04383acedca3a28530a20d917c23d8cfb57b8a0e5 eval_results.json
|
| 5 |
79219257df919cc598a75f7ae0968988ed0d33ffc1777469f9a9da39a558819d GRT_Whitepaper.pdf
|
| 6 |
5cdab387c5e2ee9bb3907acaccfccfaa7cc10060c21e3f265ce981a4a7d38226 model.safetensors
|
| 7 |
e86db55b23831f6efca3d43309863db7d3b96ec21fbf42cffa5afc35fdcc61af modules.json
|
| 8 |
+
ad8655907405547b9641880815f869a5a77ba892b08743e6e579bfef5ad20839 optimizer_ablation_results.json
|
| 9 |
48fd1319867cb7251a5568790b0bf95b4e027609630180d9097c86b0d974b3dc PROVENANCE.md
|
| 10 |
+
9818b614596016c16ef97bf5f4d962765ab081b0764b2a32f3ca0917420860a4 README.md
|
| 11 |
b344933f060b173208ed2f9b7a35c1cbf7b1a22b53cba09735bfbdbff5b04848 s1-grt-tokenizer-cache-76e9e7b2014f0479.pkl
|
| 12 |
f40b497174aa416490cf8c6e70ef19ad68f2d20160de9ca1fa8a130e89b2ee19 sentence_bert_config.json
|
| 13 |
76e9e7b2014f0479052f15dfe092fc5dfadfb52b57b8133a141a4d0deb89a6f7 tokenizer.json
|