| --- |
| language: |
| - ko |
| license: apache-2.0 |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - transformers |
| --- |
| |
| ## PwC-Embedding-expr |
|
|
| We trained the **PwC-Embedding-expr** model on top of the [multilingual-e5-large-instruct](https://huggingface.co/intfloat/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 |
| - [x] 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](https://huggingface.co/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) |
