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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# PIXIE-Rune-v1.0
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**PIXIE-Rune-v1.0** is an encoder-based embedding model trained on Korean and English triplets, developed by [TelePIX Co., Ltd](https://telepix.net/).
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**PIXIE** stands for Tele**PIX** **I**ntelligent **E**mbedding, representing TelePIX’s high-performance embedding technology.
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The model is
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It demonstrates strong performance on retrieval tasks in both languages, achieving robust results across a wide range of Korean- and English-language benchmarks.
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This makes it well-suited for real-world applications that require high-quality semantic search in Korean, English, or both.
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# PIXIE-Rune-v1.0
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**PIXIE-Rune-v1.0** is an encoder-based embedding model trained on Korean and English triplets, developed by [TelePIX Co., Ltd](https://telepix.net/).
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**PIXIE** stands for Tele**PIX** **I**ntelligent **E**mbedding, representing TelePIX’s high-performance embedding technology.
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The model is bilingual, specifically optimized for both Korean and English.
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It demonstrates strong performance on retrieval tasks in both languages, achieving robust results across a wide range of Korean- and English-language benchmarks.
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This makes it well-suited for real-world applications that require high-quality semantic search in Korean, English, or both.
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