Feature Extraction
sentence-transformers
Safetensors
xlm-roberta
sentence-similarity
dense-encoder
dense
retrieval
multimodal
multi-modal
crossmodal
cross-modal
aerospace
telepix
text-embeddings-inference
Instructions to use telepix/PIXIE-Rune-v1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use telepix/PIXIE-Rune-v1.5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("telepix/PIXIE-Rune-v1.5") 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
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### Benchmark Overview and Dataset Descriptions
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| Model Name | # params | STELLA (XL) | MTEB (ko) | RTEB (en) |
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| telepix/PIXIE-Spell-v1.5-0.6B
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| telepix/PIXIE-Spell-Preview-0.6B | 0.6B | 0.5364 | 0.7612 | 0.5722 |
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| **telepix/PIXIE-Rune-v1.5** | **0.5B** | **0.6559** | **0.7651** | **0.5546** |
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| telepix/PIXIE-Rune-v1.0 | 0.5B | 0.6345 | 0.7603 | 0.5439 |
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### Benchmark Overview and Dataset Descriptions
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| Model Name | # params | STELLA (XL) | MTEB (ko) | RTEB (en) |
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| telepix/PIXIE-Spell-v1.5-0.6B | 0.6B | 0.6731 | 0.7717 | 0.5923 |
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| telepix/PIXIE-Spell-Preview-0.6B | 0.6B | 0.5364 | 0.7612 | 0.5722 |
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| 139 |
| **telepix/PIXIE-Rune-v1.5** | **0.5B** | **0.6559** | **0.7651** | **0.5546** |
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| 140 |
| telepix/PIXIE-Rune-v1.0 | 0.5B | 0.6345 | 0.7603 | 0.5439 |
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