language, license, tags

language	license	tags
ko	apache-2.0	sentence-transformers sentence-similarity transformers

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.

⚠️ This is an experimental model and is under continuous development.

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
KLUE-STS	0.88
KLUE-TC	0.73
Ko-StrategyQA	0.80
KorSTS	0.84
MIRACL-Reranking	0.72
MIRACL-Retrieval	0.65
Average	0.77

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)