
# Example
```python
from sentence_transformers import CrossEncoder

model = CrossEncoder('ddobokki/electra-small-sts-cross-encoder')
model.predict(["그녀는 행복해서 웃었다.", "그녀는 웃겨서 눈물이 났다."])
-> 0.8206561
```
# Dataset
- KorSTS
	- Train
	- Test
- KLUE STS
	- Train
	- Test

#  Performance
| Dataset |  Pearson corr.|Spearman corr.|
|--|--|--|
| KorSTS(test) + KLUE STS(test) | 0.8528 | 0.8504 |

# TODO
Using KLUE 1.1 train, dev data



{"language": ["ko"], "tags": ["sentence_transformers", "cross_encoder"]}
transformers pytorch electra text-classification sentence_transformers cross_encoder ko autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #electra #text-classification #sentence_transformers #cross_encoder #ko #autotrain_compatible #endpoints_compatible #region-us 

Example
=======


Dataset
=======


* KorSTS
	+ Train
	+ Test
* KLUE STS
	+ Train
	+ Test


Performance
===========


Dataset: KorSTS(test) + KLUE STS(test), Pearson corr.: 0.8528, Spearman corr.: 0.8504


TODO
====


Using KLUE 1.1 train, dev data



TAGS
#transformers #pytorch #electra #text-classification #sentence_transformers #cross_encoder #ko #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #electra #text-classification #sentence_transformers #cross_encoder #ko #autotrain_compatible #endpoints_compatible #region-us 
