telepix
/

PIXIE-Spell-Preview-0.6B

Feature Extraction

sentence-transformers

sentence-similarity

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@@ -102,6 +102,7 @@ Our model, **telepix/PIXIE-Spell-Preview-0.6B**, achieves strong performance on
 | Alibaba-NLP/gte-multilingual-base | 0.3B | 0.5541 | 0.5446 | 0.5426 | 0.5574 | 0.5746 |
 | BAAI/bge-m3 | 0.5B | 0.5318 | 0.5078 | 0.5231 | 0.5389 | 0.5573 |
 | nlpai-lab/KURE-v1 | 0.5B | 0.5272 | 0.5017 | 0.5171 | 0.5353 | 0.5548 |
 | jinaai/jina-embeddings-v3 | 0.6B | 0.4482 | 0.4116 | 0.4379 | 0.4573 | 0.4861 |
 Descriptions of the benchmark datasets used for evaluation are as follows:

 | Alibaba-NLP/gte-multilingual-base | 0.3B | 0.5541 | 0.5446 | 0.5426 | 0.5574 | 0.5746 |
 | BAAI/bge-m3 | 0.5B | 0.5318 | 0.5078 | 0.5231 | 0.5389 | 0.5573 |
 | nlpai-lab/KURE-v1 | 0.5B | 0.5272 | 0.5017 | 0.5171 | 0.5353 | 0.5548 |
+| SamilPwC-AXNode-GenAI/PwC-Embedding_expr | 0.5B | 0.5111 | 0.4766 | 0.5006 | 0.5212 | 0.5460 |
 | jinaai/jina-embeddings-v3 | 0.6B | 0.4482 | 0.4116 | 0.4379 | 0.4573 | 0.4861 |
 Descriptions of the benchmark datasets used for evaluation are as follows: