SamilPwC-AXNode-GenAI/PwC-Embedding_expr

PwC-Embedding-expr

We trained the PwC-Embedding-expr model on top of the multilingual-e5-large-instruct embedding model.
To enhance performance in Korean, we applied our curated augmentation to STS datasets and fine-tuned the E5 model using a carefully balanced ratio across datasets.

To-do

• MTEB Leaderboard
• Technical Report

MTEB

PwC-Embedding_expr was evaluated on the Korean subset of MTEB.
A leaderboard link will be added once it is published.

Task	PwC-Embedding_expr	multilingual-e5-large	Max Result
KLUE-STS	0.88	0.83	0.90
KLUE-TC	0.73	0.61	0.73
Ko-StrategyQA	0.80	0.80	0.83
KorSTS	0.84	0.81	0.98
MIRACL-Reranking	0.72	0.65	0.72
MIRACL-Retrieval	0.65	0.59	0.72
Average	0.77	0.71	0.81

Model

• Base Model: intfloat/multilingual-e5-large-instruct
• Model Size: 0.56B
• Embedding Dimension: 1024
• Max Input Tokens: 514

Requirements

It works with the dependencies included in the latest version of MTEB.

Citation

TBD (technical report expected September 2025)