kpf-sbert-v1
This is a sentence-transformers 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(1,376쌍문장) 와 glue:stsb (1,500쌍문장)
- 성능 지표는 cosin.spearman
- 평가 측정 코드는 여기 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
Training
- 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=mean
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 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, eps: 1e-8, epochs: 10, train_batch: 128, eval/test_batch: 64, max_token_len: 128(교사모델이 128이므로 맟춰줌)
- 훈련코드 여기 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 참조
Citing & Authors
bongsoo
