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
Download eval_results.json from MatterWaveAI/grt-30m-embedding: direct link, hf CLI and curl.
- Browser
- Download file 462 Bytes
-
https://huggingface.co/MatterWaveAI/grt-30m-embedding/resolve/main/eval_results.json
- Command line
-
hf download hf://MatterWaveAI/grt-30m-embedding/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/MatterWaveAI/grt-30m-embedding/resolve/main/eval_results.json
462 Bytes
| { | |
| "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 | |
| } | |