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
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| Model Name | # params | Avg. NDCG | NDCG@1 | NDCG@3 | NDCG@5 | NDCG@10 |
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| telepix/PIXIE-Spell-Preview-1.7B | 1.7B | 0.
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| telepix/PIXIE-Spell-Preview-0.6B | 0.6B | 0.
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| **telepix/PIXIE-Rune-Preview** | 0.5B | **0.5781** | **0.5691** | **0.5663** | **0.5791** | **0.5979** |
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| Snowflake/snowflake-arctic-embed-l-v2.0 | 0.5B | 0.5812 | 0.5725 | 0.5705 | 0.5811 | 0.6006 |
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| Model Name | # params | Avg. NDCG | NDCG@1 | NDCG@3 | NDCG@5 | NDCG@10 |
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| telepix/PIXIE-Spell-Preview-1.7B | 1.7B | 0.5630 | 0.5446 | 0.5529 | 0.5660 | 0.5885 |
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| telepix/PIXIE-Spell-Preview-0.6B | 0.6B | 0.5354 | 0.5208 | 0.5241 | 0.5376 | 0.5589 |
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| **telepix/PIXIE-Rune-Preview** | 0.5B | **0.5781** | **0.5691** | **0.5663** | **0.5791** | **0.5979** |
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| Snowflake/snowflake-arctic-embed-l-v2.0 | 0.5B | 0.5812 | 0.5725 | 0.5705 | 0.5811 | 0.6006 |
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