metadata

license: apache-2.0
language:
  - ko

albert-small-kor-cross-encoder-v1

albert-small-kor-v1 모델을 훈련시켜 cross-encoder로 파인튜닝한 모델
This model was trained using SentenceTransformers Cross-Encoder class.

Training

sts(10)-nli(3)-sts(10)-nli(3)-sts(10) 훈련 시킴 (distil 훈련 없음)
STS : seed=111,epoch=10, lr=1e-4, eps=1e-6, warm_step=10%, max_seq_len=128, train_batch=128(small 모델=32) (albert 13m/7G) 훈련코드
NLI 훈련 : seed=111,epoch=3, lr=3e-5, eps=1e-8, warm_step=10%, max_seq_len=128, train_batch=64, eval_bath=64(albert 2h/7G) 훈련코드
평가코드,테스트코드

모델	korsts	klue-sts	glue(stsb)	stsb_multi_mt(en)
albert-small-kor-cross-encoder-v1	0.8455	0.8526	0.8513	0.7976
klue-cross-encoder-v1	0.8262	0.8833	0.8512	0.7889
kpf-cross-encoder-v1	0.8799	0.9133	0.8626	0.8027

Usage and Performance

Pre-trained models can be used like this:

from sentence_transformers import CrossEncoder
model = CrossEncoder('bongsoo/albert-small-kor-cross-encoder-v1')
scores = model.predict([('오늘 날씨가 좋다', '오늘 등산을 한다'), ('오늘 날씨가 흐리다', '오늘 비가 내린다')])
print(scores)

[0.45417202 0.6294121 ]

The model will predict scores for the pairs ('Sentence 1', 'Sentence 2') and ('Sentence 3', 'Sentence 4').

You can use this model also without sentence_transformers and by just using Transformers AutoModel class