SetFit with mini1013/master_domain

This is a SetFit model that can be used for Text Classification. This SetFit model uses mini1013/master_domain as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

  1. Fine-tuning a Sentence Transformer with contrastive learning.
  2. Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

Model Sources

Model Labels

Label Examples
5
  • '비앤진 프로텍트 미스트 앤 픽서 - 100ml x 1개 100ml × 1개 LotteOn > 뷰티 > 스킨케어 > 미스트 LotteOn > 뷰티 > 스킨케어 > 미스트'
  • '이니스프리 노세범 미네랄 파우더 5g 3.맨토스(6호-멜론) (#M)홈>화장품/미용>베이스메이크업>파우더>루스파우더 Naverstore > 화장품/미용 > 베이스메이크업 > 파우더 > 루스파우더'
  • 'LOreal Paris Magic Perfecting Base Face Primer by Studio Secrets Professional 0.50 oz (Pack of 3) One Color_One Size ssg > 뷰티 > 메이크업 > 베이스메이크업 > 메이크업베이스 ssg > 뷰티 > 메이크업 > 베이스메이크업 > 메이크업베이스'
4
  • '라이콘 스트립 왁스 800g 12종 / lycon strip wax 800g 라이코 플렉스 800g (#M)홈>호주왁싱>라이콘 Naverstore > 화장품/미용 > 바디케어 > 제모제'
  • '잉글리쉬 페어 앤 프리지아 센티드 바디 파우더 신세계백화점/헤어/바디/핸드/풋/데오/바스트케어/바스트케어;(#M)SSG.COM/바디케어/기타바디용품 LOREAL > DepartmentSsg > 아틀리에 코롱 > Generic > 여자향수'
  • '빅 오버진 에너지 380g - 배쓰 밤/입욕제 (#M)위메프 > 생활·주방용품 > 바디/헤어 > 바디케어/워시/제모 > 입욕제 위메프 > 뷰티 > 바디/헤어 > 바디케어/워시/제모 > 입욕제'
11
  • '비오템 - 오 비타미네 오드 뚜왈렛 스프레이 100ml/3.38oz LOREAL > Ssg > 비오템 > Branded > 비오템 LOREAL > Ssg > 비오템 > Branded > 비오템'
  • '[산타마리아노벨라]포푸리 바소 세라미카 멀티_F (#M)가구/인테리어>인테리어소품>아로마/캔들용품>초/향초 AD > Naverstore > smnovella브랜드스토어 > 전체상품'
  • '톰포드 오드우드 50ml LotteOn > 뷰티 > 향수 > 남녀공용향수 LotteOn > 뷰티 > 향수 > 남녀공용향수'
6
  • '[온세일] 루스 파우더 휴대용기 LotteOn > 뷰티 > 베이스메이크업 > 파우더 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 파우더'
  • '반영구재료 임뷰티 엠보니들 20pcs (1box) 1420(흰색) 홈>오늘의 타임특가!!;홈>전체상품;홈>니들;홈>한가위 추석 특가;홈>연말특가 이벤트;홈>원데이 특가상품 이벤트!;홈>주말 이벤트 특가!;(#M)홈>니들&색소 Naverstore > 화장품/미용 > 뷰티소품 > DIY화장품재료'
  • '마스터클래스 매니아 세트(브러쉬 2종+클리너) 아이M브러쉬_파운데이션브러쉬 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 브러쉬 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 브러쉬'
8
  • '푸드어홀릭 알로하와이 블렌딩 오일 150ml/태닝오일 (#M)위메프 > 생활·주방용품 > 바디/헤어 > 바디케어/워시/제모 > 태닝용품 위메프 > 뷰티 > 바디/헤어 > 바디케어/워시/제모 > 태닝용품'
  • '푸드어홀릭 알로하와이 태닝 오일(150ml) 건강한 구릿빛 피부 - 2개 택배무료 - (#M)11st>바디케어>태닝용품>오일 11st > 뷰티 > 바디케어 > 태닝용품 > 오일'
  • '대용량 SNP 아쿠아 쿨링 선 스프레이 200ml (SPF50+) (#M)홈>선크림 Naverstore > 화장품/미용 > 선케어 > 선스프레이'
9
  • '1+1 유리아쥬 오 떼르말 미스트 300ml 단품 (#M)화장품/향수>색조메이크업>립밤/립케어 Gmarket > 뷰티 > 화장품/향수 > 색조메이크업 > 립밤/립케어'
  • '메디힐 티트리 100 오일 247187 10ml × 1개 LotteOn > 뷰티 > 남성화장품 > 남성화장품세트 LotteOn > 뷰티 > 남성화장품 > 남성화장품세트'
  • '[매장 정품] 미샤 비폴렌 리뉴 오일 30ml (#M)11st>스킨케어>오일>오일 11st > 뷰티 > 스킨케어 > 오일'
1
  • '브로앤팁스 수퍼내추럴 톤업 선크림 70ml 70ml × 1개 (#M)쿠팡 홈>뷰티>스킨케어>선케어/태닝>선케어>선블록/선크림/선로션 Coupang > 뷰티 > 남성화장품 > 남성스킨케어 > 선케어'
  • '비레디 유브이 디펜스 선 베이스 프레쉬 50ml(SPF50+) (#M)홈>화장품/미용>남성화장품>선크림 Naverstore > 화장품/미용 > 남성화장품 > 선크림'
  • '랩시리즈 데일리 레스큐 에너자이징 에센스 150ml LotteOn > 뷰티 > 남성화장품 > 스킨 LotteOn > 뷰티 > 남성화장품 > 스킨'
7
  • 'P.픽싱마스카라 볼륨앤컬링 (#M)뷰티>화장품/향수>포인트메이크업>마스카라 CJmall > 뷰티 > 헤어/바디/미용기기 > 핸드/풋/덴탈케어 > 핸드케어'
  • '르 젤 쑤르실 360 블롱 ssg > 뷰티 > 메이크업 > 아이메이크업 > 아이브로우 ssg > 뷰티 > 메이크업 > 아이메이크업 > 아이브로우'
  • '조말론 런던 핸드크림 50ml 라임 바질 앤 만다린 피오니 블러쉬 잉글리쉬 페어 프리지아 (핸드크림)라임 바질 앤 만다린 50ml (#M)홈>조말론 Naverstore > 화장품/미용 > 바디케어 > 핸드케어'
13
  • '입생로랑(향수) 몽 파리 헤어 미스트 30ml MinSellAmount (#M)화장품/향수>네일케어>네일리무버 Gmarket > 뷰티 > 화장품/향수 > 네일케어 > 네일리무버'
  • '아윤채 더 리트리브 프로그램 부스팅 스타터 헤어토닉 500g × 4개 (#M)쿠팡 홈>뷰티>헤어>헤어에센스/오일>헤어토닉/스칼프 Coupang > 뷰티 > 헤어 > 헤어에센스/오일 > 헤어토닉/스칼프'
  • '아모스 04 컬링에센스 이엑스보라색150mlX3 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 헤어에센스/오일 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 헤어에센스/오일'
12
  • '컬링에센스2X 숏스타일 150ml 3개_P083181261 옵션/컬링에센스2X 숏스타일 150ml x3 ssg > 뷰티 > 헤어/바디 > 헤어스타일링 > 염색약 ssg > 뷰티 > 헤어/바디 > 헤어스타일링 > 염색약'
  • '[후후맘] 미쟝센 스테이지콜렉션 컬링 에센스 2X 230ml 230ml × 1개 (#M)쿠팡 홈>싱글라이프>샤워/세안>헤어에센스 Coupang > 뷰티 > 헤어 > 헤어에센스/오일 > 헤어에센스'
  • '휘핑컬리펌(마일드한 발림감, 힘있고 탄력있는 컬) 1제 150ml 2제 150ml 홈>펌제;홈>브랜드>[아모레레서픽]아모스;(#M)홈>파마약♡매직약>파마약 Naverstore > 화장품/미용 > 헤어스타일링 > 파마약 > 웨이브'
2
  • '베씨 시럽 컬러 젤 S04 로즈 립글로즈 × 1개 (#M)쿠팡 홈>뷰티>네일>젤네일>컬러 젤 Coupang > 뷰티 > 네일 > 젤네일 > 컬러 젤'
  • '[OPI][프로스파] 풋 파일 스트립 120 Grit DepartmentSsg > 명품화장품 > 메이크업 > 네일 DepartmentSsg > 명품화장품 > 메이크업 > 네일'
  • 'OPI 리무버 엑스퍼트터치리무버 450ml OPI 리무버 폴리쉬리무버(그린) 960ml LotteOn > 뷰티 > 네일 > 네일케어 > 네일리무버 LotteOn > 뷰티 > 네일 > 네일케어 > 네일리무버'
0
  • '다슈 데일리 에브리웨어 트래블 키트 3종 택1 02_클렌징 키트 홈>브랜드별>다슈;(#M)홈>세트 상품 Naverstore > 화장품/미용 > 클렌징 > 클렌징세트'
  • '2+1 한정수량)미니스 여행용 세트(포켓몬) MinSellAmount (#M)바디/헤어>헤어케어>샴푸/린스 Gmarket > 뷰티 > 바디/헤어 > 헤어케어 > 샴푸/린스'
  • '[동구밭] 올바른 샴푸바/린스바/설거지바 제로웨이스트 [동구밭] 배러댄리퀴드 여행세트 (#M)헤어케어>샴푸>샴푸바 AD > 11st > 뷰티 > 헤어케어 > 샴푸 > 샴푸바'
10
  • '에뛰드 마스카라 전용리무버 80ml MinSellAmount (#M)화장품/향수>클렌징/필링>립앤아이리무버 Gmarket > 뷰티 > 화장품/향수 > 클렌징/필링 > 립앤아이리무버'
  • '다나한 피부케어 효용 클렌징 크림 300ml 화장품 클렌 MinSellAmount (#M)화장품/향수>클렌징/필링>클렌징크림 Gmarket > 뷰티 > 화장품/향수 > 클렌징/필링 > 클렌징크림'
  • '대용량 곡물 페이셜 클렌징워터 500ml+파우치5매 (#M)뷰티>화장품/향수>클렌징>클렌징워터 CJmall > 뷰티 > 화장품/향수 > 클렌징 > 클렌징워터'
3
  • '바세린 수분 보습 영양 시트 마스크 팩 10매 바세린 수분 시트 마스크 10매 (#M)홈>화장품/미용>마스크/팩>마스크시트 Naverstore > 화장품/미용 > 마스크/팩 > 마스크시트'
  • '클리어 임프루브먼트 엑티브 차콜 마스크 75ml (#M)뷰티>화장품/향수>스킨케어>팩/마스크 CJmall > 뷰티 > 화장품/향수 > 스킨케어 > 팩/마스크'
  • '바세린 영양 시트 마스크23ml 10매X2 MinSellAmount (#M)화장품/향수>팩/마스크>마스크시트 Gmarket > 뷰티 > 화장품/향수 > 팩/마스크 > 마스크시트'

Evaluation

Metrics

Label Accuracy
all 0.9742

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("mini1013/master_main_item_top_bt")
# Run inference
preds = model("헤라 메이크업 픽서 (110ml)  LotteOn > 뷰티 > 스킨케어 > 미스트 LotteOn > 뷰티 > 스킨케어 > 미스트")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 10 21.9195 91
Label Training Sample Count
0 50
1 514
2 200
3 200
4 746
5 350
6 350
7 600
8 249
9 550
10 350
11 200
12 350
13 496

Training Hyperparameters

  • batch_size: (64, 64)
  • num_epochs: (30, 30)
  • max_steps: -1
  • sampling_strategy: oversampling
  • num_iterations: 100
  • body_learning_rate: (2e-05, 1e-05)
  • head_learning_rate: 0.01
  • loss: CosineSimilarityLoss
  • distance_metric: cosine_distance
  • margin: 0.25
  • end_to_end: False
  • use_amp: False
  • warmup_proportion: 0.1
  • l2_weight: 0.01
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: False

Training Results

Epoch Step Training Loss Validation Loss
0.0001 1 0.3213 -
0.0061 50 0.3448 -
0.0123 100 0.3538 -
0.0184 150 0.3436 -
0.0246 200 0.3537 -
0.0307 250 0.3403 -
0.0369 300 0.3446 -
0.0430 350 0.3437 -
0.0492 400 0.3368 -
0.0553 450 0.3344 -
0.0615 500 0.3235 -
0.0676 550 0.324 -
0.0738 600 0.3107 -
0.0799 650 0.332 -
0.0861 700 0.3147 -
0.0922 750 0.2985 -
0.0984 800 0.2989 -
0.1045 850 0.2885 -
0.1107 900 0.2791 -
0.1168 950 0.2787 -
0.1230 1000 0.2647 -
0.1291 1050 0.2546 -
0.1353 1100 0.243 -
0.1414 1150 0.232 -
0.1475 1200 0.2172 -
0.1537 1250 0.2114 -
0.1598 1300 0.1996 -
0.1660 1350 0.2017 -
0.1721 1400 0.194 -
0.1783 1450 0.1875 -
0.1844 1500 0.1961 -
0.1906 1550 0.1897 -
0.1967 1600 0.1869 -
0.2029 1650 0.1877 -
0.2090 1700 0.181 -
0.2152 1750 0.1817 -
0.2213 1800 0.185 -
0.2275 1850 0.1818 -
0.2336 1900 0.1761 -
0.2398 1950 0.1788 -
0.2459 2000 0.1782 -
0.2521 2050 0.1728 -
0.2582 2100 0.1718 -
0.2644 2150 0.1724 -
0.2705 2200 0.1668 -
0.2767 2250 0.1664 -
0.2828 2300 0.1699 -
0.2889 2350 0.1663 -
0.2951 2400 0.1599 -
0.3012 2450 0.1656 -
0.3074 2500 0.1615 -
0.3135 2550 0.1571 -
0.3197 2600 0.1579 -
0.3258 2650 0.1545 -
0.3320 2700 0.1578 -
0.3381 2750 0.1501 -
0.3443 2800 0.1614 -
0.3504 2850 0.1521 -
0.3566 2900 0.1546 -
0.3627 2950 0.1552 -
0.3689 3000 0.1509 -
0.3750 3050 0.1442 -
0.3812 3100 0.1435 -
0.3873 3150 0.1456 -
0.3935 3200 0.1429 -
0.3996 3250 0.1443 -
0.4058 3300 0.1432 -
0.4119 3350 0.1389 -
0.4180 3400 0.1387 -
0.4242 3450 0.1336 -
0.4303 3500 0.1387 -
0.4365 3550 0.1331 -
0.4426 3600 0.1361 -
0.4488 3650 0.1379 -
0.4549 3700 0.1287 -
0.4611 3750 0.1355 -
0.4672 3800 0.1279 -
0.4734 3850 0.1256 -
0.4795 3900 0.1225 -
0.4857 3950 0.1279 -
0.4918 4000 0.1198 -
0.4980 4050 0.1216 -
0.5041 4100 0.1181 -
0.5103 4150 0.1185 -
0.5164 4200 0.1166 -
0.5226 4250 0.1121 -
0.5287 4300 0.1161 -
0.5349 4350 0.1146 -
0.5410 4400 0.1111 -
0.5472 4450 0.1064 -
0.5533 4500 0.1076 -
0.5594 4550 0.1035 -
0.5656 4600 0.1016 -
0.5717 4650 0.0984 -
0.5779 4700 0.1051 -
0.5840 4750 0.0962 -
0.5902 4800 0.0928 -
0.5963 4850 0.094 -
0.6025 4900 0.0889 -
0.6086 4950 0.0881 -
0.6148 5000 0.0926 -
0.6209 5050 0.0975 -
0.6271 5100 0.0851 -
0.6332 5150 0.0916 -
0.6394 5200 0.087 -
0.6455 5250 0.086 -
0.6517 5300 0.0865 -
0.6578 5350 0.0804 -
0.6640 5400 0.0805 -
0.6701 5450 0.0815 -
0.6763 5500 0.078 -
0.6824 5550 0.079 -
0.6886 5600 0.0736 -
0.6947 5650 0.0763 -
0.7008 5700 0.0766 -
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Framework Versions

  • Python: 3.10.12
  • SetFit: 1.1.0
  • Sentence Transformers: 3.3.1
  • Transformers: 4.44.2
  • PyTorch: 2.2.0a0+81ea7a4
  • Datasets: 3.2.0
  • Tokenizers: 0.19.1

Citation

BibTeX

@article{https://doi.org/10.48550/arxiv.2209.11055,
    doi = {10.48550/ARXIV.2209.11055},
    url = {https://arxiv.org/abs/2209.11055},
    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Efficient Few-Shot Learning Without Prompts},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}
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