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PwC-Embedding_expr
SamilPwC-AXNode-GenAI/PwC-Embedding_expr
PwC-Embedding_expr is a sentence similarity model from SamilPwC-AXNode-GenAI. Use it when you need a score for how close two texts are. It is set up for sentence-transformers. The card lists the license as apache-2.0.
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 u…
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At a glance
- Task
- Sentence Similarity
- Library
- sentence-transformers
- License
- apache-2.0
- Model type
- xlm-roberta
- Access
- Public
- Created
- Aug 12, 2025
- Updated
- Aug 18, 2025
- SHA
- 6c519698
Model card
From the Hugging Face model README
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)
- Task
- Sentence Similarity
- Library
- sentence-transformers
- Type
- xlm-roberta
- License
- apache-2.0
- Languages
- ko
- Created
- Aug 12, 2025
- Updated
- Aug 18, 2025