Example
from sentence_transformers import CrossEncoder
model = CrossEncoder('ddobokki/electra-small-sts-cross-encoder')
model.predict(["๊ทธ๋
๋ ํ๋ณตํด์ ์์๋ค.", "๊ทธ๋
๋ ์๊ฒจ์ ๋๋ฌผ์ด ๋ฌ๋ค."])
-> 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
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from sentence_transformers import CrossEncoder model = CrossEncoder("ddobokki/electra-small-sts-cross-encoder") query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores)