AuroraX-Reranker-Base-v1.0

AuroraX: A Fast Cross-Lingual Reranker Bridging English and Chinese

AuroraX is a lightweight yet powerful cross-lingual reranker built upon the mmBERT-base architecture. It is designed to bridge Traditional Chinese, Simplified Chinese and English, enabling high-quality semantic ranking across languages with remarkable efficiency.

Despite having only 110M non-embedding parameters, AuroraX achieves comparable performance to state-of-the-art rerankers that are twice as large. Its design emphasizes both speed and language adaptability, making it ideal for real-world multilingual retrieval and re-ranking applications.

✨ Key Features:

• 🌏 Cross-Lingual Understanding — Trained to handle English, Traditional Chinese, and Simplified Chinese seamlessly.
• ⚡ Lightweight & Fast — Only 110M parameters (non-embedding), optimized for latency-sensitive pipelines.
• 🎯 SOTA-Level Accuracy — Comparable or superior to larger rerankers on Chinese and English benchmarks.

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Evaluation

Monolingual Benchmarks

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Cross-Lingual (ZH ↔ EN) Results

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Usage

Sentence-Transformers

from sentence_transformers import CrossEncoder

model = CrossEncoder("aqweteddy/AuroraX-Reranker-Base-v1.0")
score = model.predict([("What is Deep Learning?", "Deep learning is a subfield of ML...")])
print(score)

Text-Embedding-Inference (API)

1. Install and launch the router:

text-embeddings-router --model-id aqweteddy/AuroraX-Reranker-Base-v1.0

1. Run via REST API:

curl 127.0.0.1:8080/rerank \
  -X POST \
  -d '{"query": "What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \
  -H 'Content-Type: application/json'

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Citation

@misc{aurorax2025,
  title         = {AuroraX: A Fast Cross-Lingual Reranker Bridging English and Chinese},
  author        = {aqweteddy},
  year          = {2025},
  howpublished  = {\url{https://huggingface.co/aqweteddy/AuroraX-Reranker-Base-v1.0}},
  note          = {Lightweight and powerful eranker for English, Traditional Chinese, and Simplified Chinese}
}