Example

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

Downloads last month: 14

Safetensors

Model size

14.1M params

Tensor type

I64

F32

Inference Examples

Text Classification

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