Feature Extraction
sentence-transformers
Safetensors
xlm-roberta
sentence-similarity
dense-encoder
dense
telepix
text-embeddings-inference
Instructions to use telepix/PIXIE-Rune-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use telepix/PIXIE-Rune-Preview with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("telepix/PIXIE-Rune-Preview") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Update README.md
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@@ -102,6 +102,7 @@ Our model, **telepix/PIXIE-Rune-Preview**, achieves strong performance on a wide
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| Alibaba-NLP/gte-multilingual-base | 0.3B | 0.5541 | 0.5446 | 0.5426 | 0.5574 | 0.5746 |
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| BAAI/bge-m3 | 0.5B | 0.5318 | 0.5078 | 0.5231 | 0.5389 | 0.5573 |
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| nlpai-lab/KURE-v1 | 0.5B | 0.5272 | 0.5017 | 0.5171 | 0.5353 | 0.5548 |
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| jinaai/jina-embeddings-v3 | 0.6B | 0.4482 | 0.4116 | 0.4379 | 0.4573 | 0.4861 |
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Descriptions of the benchmark datasets used for evaluation are as follows:
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| Alibaba-NLP/gte-multilingual-base | 0.3B | 0.5541 | 0.5446 | 0.5426 | 0.5574 | 0.5746 |
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| BAAI/bge-m3 | 0.5B | 0.5318 | 0.5078 | 0.5231 | 0.5389 | 0.5573 |
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| nlpai-lab/KURE-v1 | 0.5B | 0.5272 | 0.5017 | 0.5171 | 0.5353 | 0.5548 |
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| SamilPwC-AXNode-GenAI/PwC-Embedding_expr | 0.5B | 0.5111 | 0.4766 | 0.5006 | 0.5212 | 0.5460 |
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| jinaai/jina-embeddings-v3 | 0.6B | 0.4482 | 0.4116 | 0.4379 | 0.4573 | 0.4861 |
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Descriptions of the benchmark datasets used for evaluation are as follows:
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