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Model: telepix/PIXIE-Rune-v1.0 Source: Original Platform
2026-05-14 17:04:15 +08:00
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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- dense-encoder
+- dense
+- feature-extraction
+- retrieval
+- multimodal
+- multi-modal
+- crossmodal
+- cross-modal
+- aerospace
+- telepix
+language:
+- af
+- ar
+- az
+- be
+- bg
+- bn
+- ca
+- ceb
+- cs
+- cy
+- da
+- de
+- el
+- en
+- es
+- et
+- eu
+- fa
+- fi
+- fr
+- gl
+- gu
+- he
+- hi
+- hr
+- ht
+- hu
+- hy
+- id
+- is
+- it
+- ja
+- jv
+- ka
+- kk
+- km
+- kn
+- ko
+- ky
+- lo
+- lt
+- lv
+- mk
+- ml
+- mn
+- mr
+- ms
+- my
+- ne
+- nl
+- pa
+- pl
+- pt
+- qu
+- ro
+- ru
+- si
+- sk
+- sl
+- so
+- sq
+- sr
+- sv
+- sw
+- ta
+- te
+- th
+- tl
+- tr
+- uk
+- ur
+- vi
+- yo
+- zh
+pipeline_tag: feature-extraction
+library_name: sentence-transformers
+license: apache-2.0
+---
+<p align="center">
+    <img src="https://cdn-uploads.huggingface.co/production/uploads/61d6f4a4d49065ee28a1ee7e/V8n2En7BlMNHoi1YXVv8Q.png" width="400"/>
+<p>
+
+# PIXIE-Rune-v1.0
+**PIXIE-Rune-v1.0** is an encoder-based embedding model trained on Korean and English information retrieval dataset, 
+developed by [TelePIX Co., Ltd](https://telepix.net/).
+**PIXIE** stands for Tele**PIX** **I**ntelligent **E**mbedding, representing TelePIX’s high-performance embedding technology.
+This model is specifically optimized for semantic retrieval tasks in Korean and English, and demonstrates strong performance in aerospace domain. Through extensive fine-tuning and domain-specific evaluation, PIXIE shows robust retrieval quality for real-world use cases such as document understanding, technical QA, and semantic search in aerospace and related high-precision fields.
+It also performs competitively across a wide range of open-domain Korean and English retrieval benchmarks, making it a versatile foundation for multilingual semantic search systems.
+
+
+## Model Description
+- **Model Type:** Sentence Transformer
+<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
+- **Maximum Sequence Length:** 6144 tokens
+- **Output Dimensionality:** 1024 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Language:** Multilingual — optimized for high performance in Korean and English
+- **Domain Specialization:** Aerospace Information Retrieval
+- **License:** apache-2.0 
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 6144, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
+  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Quality Benchmarks
+**PIXIE-Rune-v1.0** is a multilingual embedding model specialized for Korean and English retrieval tasks. 
+It delivers consistently strong performance across a diverse set of domain-specific and open-domain benchmarks in both languages, demonstrating its effectiveness in real-world semantic search applications.
+The table below presents the retrieval performance of several embedding models evaluated on a variety of Korean and English benchmarks.
+We report **Normalized Discounted Cumulative Gain (nDCG@10)** scores, which measure how well a ranked list of documents aligns with ground truth relevance. Higher values indicate better retrieval quality.  
+  
+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@10 computation across models.
+
+### Benchmark Overview and Dataset Descriptions
+| Model Name | # params | STELLA (XL) | MTEB (ko) | BEIR (en) |
+|------|:---:|:---:|:---:|:---:|
+| **telepix/PIXIE-Rune-v1.0** | **0.5B** | **0.6345** | **0.7603** | **0.5872** |
+| | | | | |
+| nvidia/llama-embed-nemotron-8b | 8B | 0.7181 | 0.7813 | 0.6935 |
+| Qwen/Qwen3-Embedding-8B | 8B | 0.6154 | 0.7839 | 0.6701 |
+| Snowflake/snowflake-arctic-embed-l-v2.0 | 0.5B | 0.5448 | 0.7390 | 0.6006 |
+| BAAI/bge-m3 | 0.5B | 0.5056 | 0.7483 | 0.5573 |
+| Qwen/Qwen3-Embedding-0.6B | 0.6B | 0.4707 | 0.7017 | 0.5839 |
+| Octen/Octen-Embedding-0.6B | 0.6B | 0.4683 | 0.7057 | 0.5769 |
+| Salesforce/SFR-Embedding-Mistral | 7B | 0.4579 | N/A | N/A |
+| Alibaba-NLP/gte-multilingual-base | 0.3B | 0.4097 | 0.7084 | 0.5746 |
+| intfloat/multilingual-e5-large-instruct | 0.6B | 0.2384 | 0.7050 | N/A |
+| jinaai/jina-embeddings-v3 | 0.5B | N/A | 0.7088 | 0.4861 |
+| openai/text-embedding-3-large | N/A | N/A | 0.6646 | N/A |
+
+To better interpret the evaluation results above, we briefly describe the characteristics and evaluation intent of each benchmark suite used in this comparison.
+Each benchmark is designed to assess different aspects of retrieval capability, ranging from domain-specific technical understanding to open-domain and multilingual generalization.
+
+#### STELLA
+[STELLA](https://arxiv.org/abs/2601.03496) is an aerospace-domain Information Retrieval (IR) benchmark constructed from NASA Technical Reports Server (NTRS) documents. It is designed to evaluate both:
+
+- **Lexical matching** ability (does the retriever benefit from exact technical terms? | TCQ)
+- **Semantic matching** ability (can the retriever match concepts even when technical terms are not explicitly used? | TAQ).
+
+STELLA provides **dual-type synthetic queries** and a **cross-lingual extension** for multilingual evaluation while keeping the corpus in English.
+
+#### 6 Datasets of MTEB (Korean)
+Descriptions of the benchmark datasets used for evaluation are as follows:
+- **Ko-StrategyQA**  
+  A Korean multi-hop open-domain question answering dataset designed for complex reasoning over multiple documents.
+- **AutoRAGRetrieval**  
+  A domain-diverse retrieval dataset covering finance, government, healthcare, legal, and e-commerce sectors.
+- **MIRACLRetrieval**  
+  A document retrieval benchmark built on Korean Wikipedia articles.
+- **PublicHealthQA**  
+  A retrieval dataset focused on medical and public health topics.
+- **BelebeleRetrieval**  
+  A dataset for retrieving relevant content from web and news articles in Korean.
+- **MultiLongDocRetrieval**  
+  A long-document retrieval benchmark based on Korean Wikipedia and mC4 corpus.
+
+#### 7 Datasets of BEIR (English)
+Descriptions of the benchmark datasets used for evaluation are as follows:
+- **ArguAna**  
+  A dataset for argument retrieval based on claim-counterclaim pairs from online debate forums.
+- **FEVER**  
+  A fact verification dataset using Wikipedia for evidence-based claim validation.
+- **FiQA-2018**  
+  A retrieval benchmark tailored to the finance domain with real-world questions and answers.
+- **HotpotQA**  
+  A multi-hop open-domain QA dataset requiring reasoning across multiple documents.
+- **MSMARCO**  
+  A large-scale benchmark using real Bing search queries and corresponding web documents.
+- **NQ**  
+  A Google QA dataset where user questions are answered using Wikipedia articles.
+- **SCIDOCS**  
+  A citation-based document retrieval dataset focused on scientific papers.
+  
+## Direct Use (Semantic Search)
+
+```python
+from sentence_transformers import SentenceTransformer
+  
+# Load the model
+model_name = 'telepix/PIXIE-Rune-v1.0'
+model = SentenceTransformer(model_name)
+
+# Define the queries and documents
+queries = [
+        "텔레픽스는 어떤 산업 분야에서 위성 데이터를 활용하나요?",
+        "국방 분야에 어떤 위성 서비스가 제공되나요?",
+        "텔레픽스의 기술 수준은 어느 정도인가요?",
+]
+documents = [
+        "텔레픽스는 해양, 자원, 농업 등 다양한 분야에서 위성 데이터를 분석하여 서비스를 제공합니다.",
+        "정찰 및 감시 목적의 위성 영상을 통해 국방 관련 정밀 분석 서비스를 제공합니다.",
+        "TelePIX의 광학 탑재체 및 AI 분석 기술은 Global standard를 상회하는 수준으로 평가받고 있습니다.",
+        "텔레픽스는 우주에서 수집한 정보를 분석하여 '우주 경제(Space Economy)'라는 새로운 가치를 창출하고 있습니다.",
+        "텔레픽스는 위성 영상 획득부터 분석, 서비스 제공까지 전 주기를 아우르는 솔루션을 제공합니다.",
+]
+
+# Compute embeddings: use `prompt_name="query"` to encode queries!
+query_embeddings = model.encode(queries, prompt_name="query")
+document_embeddings = model.encode(documents)
+
+# Compute cosine similarity scores
+scores = model.similarity(query_embeddings, document_embeddings)
+
+# Output the results
+for query, query_scores in zip(queries, scores):
+    doc_score_pairs = list(zip(documents, query_scores))
+    doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
+    print("Query:", query)
+    for document, score in doc_score_pairs:
+        print(score, document)
+
+```
+
+## License
+The PIXIE-Rune-v1.0 model is licensed under Apache License 2.0.
+
+## Citation
+```
+@misc{TelePIX-PIXIE-Rune-v1.0,
+  title={PIXIE-Rune-v1.0},
+  author={TelePIX AI Research Team and Bongmin Kim},
+  year={2026},
+  url={https://huggingface.co/telepix/PIXIE-Rune-v1.0}
+}
+```
+
+## Contact
+
+If you have any suggestions or questions about the PIXIE, please reach out to the authors at bmkim@telepix.net.