|
--- |
|
language: |
|
- ko |
|
--- |
|
pipeline_tag: sentence-similarity |
|
tags: |
|
- sentence-transformers |
|
- sentence-similarity |
|
- transformers |
|
--- |
|
|
|
# leewaay/kpf-bert-base-klueSTS-cross |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model: A cross encoder trained with the pretrained [`jinmang2/kpfbert`](https://huggingface.co/jinmang2/kpfbert) model on the [KLUE STS dataset](https://huggingface.co/datasets/klue#sts) for sentence similarity tasks. It's specifically designed for direct evaluation of sentence pairs, making it highly effective for [Re-Ranking](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) and [Augmented SBERT](https://www.sbert.net/examples/training/data_augmentation/README.html) for data labeling tasks aimed at enhancing SBERT. |
|
|
|
## Usage (Sentence-Transformers) |
|
|
|
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: |
|
|
|
```bash |
|
pip install -U sentence-transformers |
|
``` |
|
|
|
Then you can use the model like this: |
|
|
|
```python |
|
from sentence_transformers import CrossEncoder |
|
pairs = [('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')] |
|
|
|
model = CrossEncoder('leewaay/kpf-bert-base-klueSTS-cross') |
|
scores = model.predict(pairs) |
|
print(scores) |
|
``` |
|
|
|
## Citing & Authors |
|
|
|
[Wonseok Lee](https://github.com/leewaay) |
