Update README.md

README.md

@@ -31,6 +31,30 @@ jinmang2/kpfbert 모델을 sentencebert로 파인듀닝한 모델
 
 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