readme: ---
  pipeline_tag: sentence-similarity
  tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
  ---
  
  # kpf-sbert-v1
  
  This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
  
  jinmang2/kpfbert 모델을 sentencebert로 파인듀닝한 모델
  
  ## Evaluation Results
  
  - 성능 측정을 위한 말뭉치는,  아래 한국어 (kor), 영어(en)  평가 말뭉치를 이용함
  <br> 한국어 : **korsts(1,379쌍문장)** 와 **klue-sts(519쌍문장)** 
  <br> 영어 : [stsb_multi_mt](https://huggingface.co/datasets/stsb_multi_mt)(1,376쌍문장) 와 [glue:stsb](https://huggingface.co/datasets/glue/viewer/stsb/validation) (1,500쌍문장)
  - 성능 지표는 **cosin.spearman**
  - 평가 측정 코드는 [여기](https://github.com/kobongsoo/BERT/blob/master/sbert/sbert-test3.ipynb) 참조
  - 
  |모델     |korsts|klue-sts|glue(stsb)|stsb_multi_mt(en)|
  |:--------|------:|--------:|--------------:|------------:|
  |distiluse-base-multilingual-cased-v2   |0.7475    |0.7855    |0.8193           |0.8075|
  |paraphrase-multilingual-mpnet-base-v2  |0.8201    |0.7993    |0.8907           |0.8682|
  |bongsoo/albert-small-kor-sbert-v1      |0.8305    |0.8588    |0.8419           |0.7965|
  |bongsoo/klue-sbert-v1.0                |0.8529    |0.8952    |0.8813           |0.8469|
  |**bongsoo/kpf-sbert-v1.0**                 |0.8590    |0.8924    |0.8840           |0.8531|
  
  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
  
  ## Training
  - [jinmang2/kpfbert](https://huggingface.co/jinmang2/kpfbert) 모델을 sts(10)-distil(10)-nli(3)-sts(10) 훈련 시킴
  
  The model was trained with the parameters:
  
  **공통**
  - **do_lower_case=1, correct_bios=0, polling_mode=cls**
    
  **1.STS**
  - 말뭉치 : korsts(5,749) + kluestsV1.1(11,668) + stsb_multi_mt(5,749) + mteb/sickr-sts(9,927) + glue stsb(5,749) (총:38,842)
  - Param : **lr: 1e-4, eps: 1e-6, warm_step=10%, epochs: 10, train_batch: 128, eval_batch: 64, max_token_len: 72**
  - 훈련코드 [여기](https://github.com/kobongsoo/BERT/blob/master/sbert/sentece-bert-sts.ipynb) 참조
      
  **2.distilation**
  - 교사 모델 : paraphrase-multilingual-mpnet-base-v2(max_token_len:128)
  - 말뭉치 : news_talk_en_ko_train.tsv (영어-한국어 대화-뉴스 병렬 말뭉치 : 1.38M)
  - Param : **lr: 5e-5, epochs: 10, train_batch: 128, eval/test_batch: 64, max_token_len: 128(교사모델이 128이므로 맟춰줌)**
  - 훈련코드 [여기](https://github.com/kobongsoo/BERT/blob/master/sbert/sbert-distillaton.ipynb) 참조
  
  **3.NLI**
  - 말뭉치 : 훈련(967,852) : kornli(550,152), kluenli(24,998), glue-mnli(392,702) / 평가(3,519) : korsts(1,500), kluests(519), gluests(1,500) ()
  - HyperParameter : **lr: 3e-5, eps: 1e-8, warm_step=10%, epochs: 3, train/eval_batch: 64, max_token_len: 128**
  - 훈련코드 [여기](https://github.com/kobongsoo/BERT/blob/master/sbert/sentence-bert-nli.ipynb) 참조
  - 
  ## Citing & Authors
  
  bongsoo