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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@@ -46,6 +46,7 @@ The table below presents the retrieval performance of several embedding models e
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We report **Normalized Discounted Cumulative Gain (NDCG)** scores, which measure how well a ranked list of documents aligns with ground truth relevance. Higher values indicate better retrieval quality.
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- **Avg. NDCG**: Average of NDCG@1, @3, @5, and @10 across all benchmark datasets.
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- **NDCG@k**: Relevance quality of the top-*k* retrieved results.
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All evaluations were conducted using the open-source **[Korean-MTEB-Retrieval-Evaluators](https://github.com/BM-K/Korean-MTEB-Retrieval-Evaluators)** codebase to ensure consistent dataset handling, indexing, retrieval, and NDCG@k computation across models.
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#### 7 Datasets of MTEB (Korean)
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We report **Normalized Discounted Cumulative Gain (NDCG)** scores, which measure how well a ranked list of documents aligns with ground truth relevance. Higher values indicate better retrieval quality.
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- **Avg. NDCG**: Average of NDCG@1, @3, @5, and @10 across all benchmark datasets.
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- **NDCG@k**: Relevance quality of the top-*k* retrieved results.
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All evaluations were conducted using the open-source **[Korean-MTEB-Retrieval-Evaluators](https://github.com/BM-K/Korean-MTEB-Retrieval-Evaluators)** codebase to ensure consistent dataset handling, indexing, retrieval, and NDCG@k computation across models.
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#### 7 Datasets of MTEB (Korean)
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