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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README.md
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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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## Model
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### Model Description
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- **Model Type:** Sentence Transformer
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<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
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- **Maximum Sequence Length:** 8192 tokens
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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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## Model Description
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- **Model Type:** Sentence Transformer
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<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
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- **Maximum Sequence Length:** 8192 tokens
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