README.md

metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - telepix
pipeline_tag: sentence-similarity
library_name: sentence-transformers

PIXIE-Rune-M-v1.0

PIXIE-Rune-M-v1.0 is an encoder-based embedding model trained on Korean and English triplets, developed by TelePIX Co., Ltd. PIXIE stands for TelxPIX Intelligent Embedding, representing TelePIX’s high-performance embedding technology. The model is multilingual, specifically optimized for both Korean and English. It demonstrates strong performance on retrieval tasks in both languages, achieving robust results across a wide range of Korean- and English-language benchmarks. This makes it well-suited for real-world applications that require high-quality semantic search in Korean, English, or both.

Model Description

• Model Type: Sentence Transformer
• Maximum Sequence Length: 8192 tokens
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity
• Language: Multilingual — optimized for high performance in Korean and English
• License: apache-2.0

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, '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()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer
  
# Load the model
model_name = 'PIXIE-Rune-M-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 국방, 농업, 자원, 해양 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)

Framework Versions

• Python: 3.10.16
• Sentence Transformers: 4.0.1
• Transformers: 4.51.3
• PyTorch: 2.6.0+cu124
• Accelerate: 1.5.2
• Datasets: 2.21.0
• Tokenizers: 0.21.1

Contact

If you have any suggestions or questions about this Model, please reach out to the authors at bmkim@telepix.net.