readme: ---
  license: apache-2.0
  language:
  - ko
  ---
  # klue-cross-encoder-v1
  - klue/bert-base 모델을 훈련시켜 cross-encoder로 파인튜닝한 모델
  - This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
  
  # Training
  - sts(10)-sts(10)훈련 시킴 
  - 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](https://github.com/kobongsoo/BERT/blob/master/sbert/cross-encoder/sbert-corossencoder-train-nli.ipynb) 
  - [평가코드](https://github.com/kobongsoo/BERT/blob/master/sbert/cross-encoder/sbert-crossencoder-test3.ipynb),[테스트코드](https://github.com/kobongsoo/BERT/blob/master/sbert/cross-encoder/sbert-crossencoder-test.ipynb)
  
  - 
  |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/kpf-cross-encoder-v1')
  scores = model.predict([('오늘 날씨가 좋다', '오늘 등산을 한다'), ('오늘 날씨가 흐리다', '오늘 비가 내린다')])
  print(scores)
  ```
  ```
  [0.10161418 0.45563662]
  ```
  
  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
  