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
3
  • '[묶음할인~25%+T11%]에뛰드 타임어택 ~60% 전품목 빅세일/호랑이의 해 무직타이거 콜라보 런칭 50.패치(1)_매끈반짝3단코팩5개_650000010398 쇼킹딜 홈>뷰티>선케어/메이크업>아이메이크업;11st>메이크업>아이메이크업>아이섀도우;11st>뷰티>선케어/메이크업>아이메이크업;11st Hour Event > 패션/뷰티 > 뷰티 > 선케어/메이크업 > 아이메이크업 11st Hour Event > 패션/뷰티 > 뷰티 > 선케어/메이크업 > 아이메이크업'
  • '차앤박 안티포어 블랙헤드 클리어 키트 스트립 (#M)홈>화장품/미용>마스크/팩>코팩 Naverstore > 화장품/미용 > 마스크/팩 > 코팩'
  • '[차앤박] CNP 안티포어 블랙헤드 클리어 키트 스트립 3세트(3회분) (#M)위메프 > 뷰티 > 스킨케어 > 팩/마스크 > 코팩 위메프 > 뷰티 > 스킨케어 > 팩/마스크 > 코팩'
0
  • '[10%+15%]한스킨 6월 클리어런스 클렌징오일/토너패드/에센스/블랙헤드/마스크~81%OF 블레미쉬 커버 컨실러_브라이트 [GH990355] 쇼킹딜 홈>뷰티>선케어/메이크업>페이스메이크업;11st>뷰티>선케어/메이크업>페이스메이크업;11st>메이크업>페이스메이크업>BB크림;11st > 뷰티 > 메이크업 > 페이스메이크업 11st Hour Event > 패션/뷰티 > 뷰티 > 선케어/메이크업 > 페이스메이크업'
  • '네이처리퍼블릭 [네이처리퍼블릭][1+1]수딩 앤 모이스처 알로에베라 수딩젤 마스크시트 단일옵션 × 선택완료 쿠팡 홈>뷰티>스킨케어>마스크/팩>코팩/기타패치>기타패치;Coupang > 뷰티 > 로드샵 > 스킨케어 > 마스크/팩 > 코팩/기타패치 > 기타패치;(#M)쿠팡 홈>뷰티>스킨케어>마스크/팩>패치/코팩>기타패치 Coupang > 뷰티 > 스킨케어 > 마스크/팩 > 패치/코팩 > 기타패치'
  • '이니스프리 블랙티 유스 인핸싱 앰플 마스크 28ml 1개입 × 5개 LotteOn > 뷰티 > 스킨케어 > 마스크/팩 > 마스크팩 LotteOn > 뷰티 > 스킨케어 > 마스크/팩 > 마스크팩'
2
  • '[쿠폰30%+스토어10%]에뛰드 ~64% 21년 신제품 앵콜전(플레이컬러아이즈/그림자쉐딩/픽싱틴트/순정) 58.AC 클린업_핑크마스크_111080503 쇼킹딜 홈>뷰티>스킨케어>크림;쇼킹딜 홈>뷰티>스킨케어>스킨/로션;11st>스킨케어>스킨/토너>스킨/토너;11st>메이크업>아이메이크업>아이섀도우;쇼킹딜 홈>뷰티>선케어/메이크업>아이메이크업;11st>뷰티>선케어/메이크업>아이메이크업;11st > timedeal 11st Hour Event > 패션/뷰티 > 뷰티 > 선케어/메이크업 > 아이메이크업'
  • '마스크 오브 매그너민티 315g 파워 마스크 (#M)뷰티>헤어/바디/미용기기>헤어케어>기획세트 CJmall > 뷰티 > 헤어/바디/미용기기 > 헤어스타일링 > 왁스/스프레이'
  • '[말썽피부케어추천] 쑥뜸팩+쑥카밍젤 (#M)위메프 > 뷰티 > 클렌징/필링 > 필링젤/스크럽 > 필링젤/스크럽 위메프 > 뷰티 > 클렌징/필링 > 필링젤/스크럽 > 필링젤/스크럽'
1
  • '티르티르 물광 콜라겐 生생크림 버블팩 물광마스크 노워시 80ml 당일출고 티르티르콜라겐80ml (#M)홈>화장품/미용>스킨케어>크림 Naverstore > 화장품/미용 > 스킨케어 > 크림'
  • "달바 모델 한혜진's pick 화이트트러플 세럼 7통+아이크림1통 단일상품 TV쇼핑>TV쇼핑 화장품/이미용>화장품/향수>기초스킨케어;(#M)TV상품>TV쇼핑 화장품/이미용>화장품/향수>기초스킨케어 CJmall > 뷰티 > 화장품/향수 > 더모코스메틱 > 에센스/세럼/오일"
  • '시슬리 벨벳 슬리핑 마스크 LotteOn > 뷰티 > 남성화장품 > 남성화장품세트 LotteOn > 뷰티 > 남성화장품 > 남성화장품세트'

Evaluation

Metrics

Label Accuracy
all 0.5683

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_cate_bt_top3_test")
# Run inference
preds = model("메디힐 티트리 케어솔루션 에센셜 마스크 이엑스  LotteOn > 뷰티 > 마스크/팩 > 마스크팩 LotteOn > 뷰티 > 마스크/팩 > 마스크팩")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 12 22.655 91
Label Training Sample Count
0 50
1 50
2 50
3 50

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
0.0032 1 0.478 -
0.1597 50 0.4392 -
0.3195 100 0.4128 -
0.4792 150 0.3767 -
0.6390 200 0.3406 -
0.7987 250 0.2889 -
0.9585 300 0.2482 -
1.1182 350 0.2336 -
1.2780 400 0.1948 -
1.4377 450 0.1284 -
1.5974 500 0.0958 -
1.7572 550 0.0893 -
1.9169 600 0.0788 -
2.0767 650 0.0706 -
2.2364 700 0.058 -
2.3962 750 0.0476 -
2.5559 800 0.0406 -
2.7157 850 0.0327 -
2.8754 900 0.0198 -
3.0351 950 0.0183 -
3.1949 1000 0.0131 -
3.3546 1050 0.0093 -
3.5144 1100 0.005 -
3.6741 1150 0.0004 -
3.8339 1200 0.0001 -
3.9936 1250 0.0001 -
4.1534 1300 0.0 -
4.3131 1350 0.0001 -
4.4728 1400 0.0 -
4.6326 1450 0.0 -
4.7923 1500 0.0 -
4.9521 1550 0.0 -
5.1118 1600 0.0 -
5.2716 1650 0.0006 -
5.4313 1700 0.0001 -
5.5911 1750 0.0 -
5.7508 1800 0.0 -
5.9105 1850 0.0 -
6.0703 1900 0.0 -
6.2300 1950 0.0 -
6.3898 2000 0.0 -
6.5495 2050 0.0 -
6.7093 2100 0.0 -
6.8690 2150 0.0 -
7.0288 2200 0.0 -
7.1885 2250 0.0 -
7.3482 2300 0.0 -
7.5080 2350 0.0 -
7.6677 2400 0.0 -
7.8275 2450 0.0 -
7.9872 2500 0.0 -
8.1470 2550 0.0 -
8.3067 2600 0.0002 -
8.4665 2650 0.0 -
8.6262 2700 0.0 -
8.7859 2750 0.0001 -
8.9457 2800 0.0 -
9.1054 2850 0.0 -
9.2652 2900 0.0 -
9.4249 2950 0.0002 -
9.5847 3000 0.0096 -
9.7444 3050 0.0007 -
9.9042 3100 0.0006 -
10.0639 3150 0.0005 -
10.2236 3200 0.0001 -
10.3834 3250 0.0018 -
10.5431 3300 0.0003 -
10.7029 3350 0.0003 -
10.8626 3400 0.0 -
11.0224 3450 0.0016 -
11.1821 3500 0.0058 -
11.3419 3550 0.0055 -
11.5016 3600 0.005 -
11.6613 3650 0.0062 -
11.8211 3700 0.0017 -
11.9808 3750 0.0002 -
12.1406 3800 0.0001 -
12.3003 3850 0.0 -
12.4601 3900 0.0 -
12.6198 3950 0.0 -
12.7796 4000 0.0 -
12.9393 4050 0.0 -
13.0990 4100 0.0 -
13.2588 4150 0.0 -
13.4185 4200 0.0 -
13.5783 4250 0.0 -
13.7380 4300 0.0 -
13.8978 4350 0.0 -
14.0575 4400 0.0 -
14.2173 4450 0.0 -
14.3770 4500 0.0 -
14.5367 4550 0.0 -
14.6965 4600 0.0 -
14.8562 4650 0.0 -
15.0160 4700 0.0 -
15.1757 4750 0.0 -
15.3355 4800 0.0 -
15.4952 4850 0.0 -
15.6550 4900 0.0 -
15.8147 4950 0.0 -
15.9744 5000 0.0 -
16.1342 5050 0.0 -
16.2939 5100 0.0 -
16.4537 5150 0.0 -
16.6134 5200 0.0 -
16.7732 5250 0.0 -
16.9329 5300 0.0 -
17.0927 5350 0.0 -
17.2524 5400 0.0 -
17.4121 5450 0.0 -
17.5719 5500 0.0 -
17.7316 5550 0.0 -
17.8914 5600 0.0 -
18.0511 5650 0.0 -
18.2109 5700 0.0 -
18.3706 5750 0.0 -
18.5304 5800 0.0 -
18.6901 5850 0.0 -
18.8498 5900 0.0 -
19.0096 5950 0.0 -
19.1693 6000 0.0 -
19.3291 6050 0.0 -
19.4888 6100 0.0 -
19.6486 6150 0.0 -
19.8083 6200 0.0 -
19.9681 6250 0.0 -
20.1278 6300 0.0 -
20.2875 6350 0.0 -
20.4473 6400 0.0 -
20.6070 6450 0.0 -
20.7668 6500 0.0 -
20.9265 6550 0.0 -
21.0863 6600 0.0 -
21.2460 6650 0.0 -
21.4058 6700 0.0 -
21.5655 6750 0.0 -
21.7252 6800 0.0 -
21.8850 6850 0.0 -
22.0447 6900 0.0 -
22.2045 6950 0.0 -
22.3642 7000 0.0 -
22.5240 7050 0.0 -
22.6837 7100 0.0 -
22.8435 7150 0.0 -
23.0032 7200 0.0 -
23.1629 7250 0.0 -
23.3227 7300 0.0 -
23.4824 7350 0.0 -
23.6422 7400 0.0 -
23.8019 7450 0.0 -
23.9617 7500 0.0 -
24.1214 7550 0.0 -
24.2812 7600 0.0 -
24.4409 7650 0.0 -
24.6006 7700 0.0 -
24.7604 7750 0.0 -
24.9201 7800 0.0 -
25.0799 7850 0.0 -
25.2396 7900 0.0 -
25.3994 7950 0.0 -
25.5591 8000 0.0 -
25.7188 8050 0.0 -
25.8786 8100 0.0 -
26.0383 8150 0.0 -
26.1981 8200 0.0 -
26.3578 8250 0.0 -
26.5176 8300 0.0 -
26.6773 8350 0.0 -
26.8371 8400 0.0 -
26.9968 8450 0.0 -
27.1565 8500 0.0 -
27.3163 8550 0.0 -
27.4760 8600 0.0 -
27.6358 8650 0.0 -
27.7955 8700 0.0 -
27.9553 8750 0.0 -
28.1150 8800 0.0 -
28.2748 8850 0.0 -
28.4345 8900 0.0 -
28.5942 8950 0.0 -
28.7540 9000 0.0 -
28.9137 9050 0.0 -
29.0735 9100 0.0 -
29.2332 9150 0.0 -
29.3930 9200 0.0 -
29.5527 9250 0.0 -
29.7125 9300 0.0 -
29.8722 9350 0.0 -

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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