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master_cate_bt7_test_flat_top_cate

Text Classification
setfit
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sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results
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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
2
  • '이니스프리 노세범 선쿠션 SPF50+ PA++++ 14g × 8개 LotteOn > 뷰티 > 스킨케어 > 선케어 > 선크림 LotteOn > 뷰티 > 스킨케어 > 선케어 > 선크림'
  • '1+1 / 달바 비건 톤업 선쿠션 15gX2개 / 촉촉 간편하게 혼합자차 핑크빛 물광 윤광 / 베이스 프리 데일리 필수템 [단품] 톤업 선쿠션 15g(1개) (#M)홈>제품유형별>선케어 Naverstore > 화장품/미용 > 선케어 > 선파우더/쿠션'
  • 'UV 프로텍티브 컴팩트 파운데이션(리필+케이스) SPF35/PA+++ 12g 페어 아이보리 DepartmentLotteOn > 뷰티 > 스킨케어 > 선케어 > 선크림/선로션 DepartmentLotteOn > 뷰티 > 스킨케어 > 선케어 > 선크림/선로션'
1
  • '비쉬 까삐탈 솔레일 선 스프레이 LSF50 200ml ssg > 뷰티 > 스킨케어 > 선케어 > 선크림 ssg > 뷰티 > 스킨케어 > 선케어 > 선크림'
  • '끈적임없는 SNP 선스프레이 자외선차단 쿨링 뿌리는 선크림 (#M)홈>화장품/미용>선케어>선스프레이 Naverstore > 화장품/미용 > 선케어 > 선스프레이'
  • '리더스 자외선차단 썬버디 올 오버 선 스프레이 180ml MinSellAmount (#M)화장품/향수>선케어>선스프레이 Gmarket > 뷰티 > 화장품/향수 > 선케어 > 선스프레이'
0
  • '[AHC 썸머세일] 박세리감독 기획 마스터즈 에어리치 선스틱 14g+[ ] 마... 옵션선택:002P01)선스틱 LotteOn > 뷰티 > 헤어/바디 > 바디케어 > 바디케어용품 LotteOn > 뷰티 > 헤어/바디 > 바디케어 > 바디케어용품'
  • '3CE PRIMER SUN STICK 프라이머 선스틱 BEIGE_FRE ssg > 뷰티 > 메이크업 > 립메이크업 > 립스틱;ssg > 뷰티 > 스킨케어 > 선케어 > 선스틱 ssg > 뷰티 > 스킨케어 > 선케어'
  • '에스케이-투 피테라 풀라인 세트 1개 Coupang > 뷰티 > 선물세트/키트 > 선물세트 > 스킨케어;쿠팡 홈;(#M)쿠팡 홈>뷰티>선물세트/키트>선물세트>스킨케어 Coupang > 뷰티 > 선물세트/키트 > 선물세트 > 스킨케어'
4
  • '마몽드 로즈워터 수딩젤 300 ml ssg > 뷰티 > 스킨케어 > 크림 ssg > 뷰티 > 스킨케어 > 크림'
  • '푸드어홀릭 알로에/스네일 수딩젤 300ml MinSellAmount 화장품/향수>크림>안티에이징크림;(#M)화장품/향수>스킨케어>크림/젤 Gmarket > 뷰티 > 화장품/향수 > 스킨케어 > 크림/젤'
  • '메디플라워 알로에베라 프레시 수딩젤 300ml 300ml × 1개 쿠팡 홈>뷰티>스킨케어>기초화장품>크림/올인원>페이셜크림;쿠팡 홈;Coupang > 뷰티 > 스킨케어 > 기초화장품;(#M)쿠팡 홈>뷰티>스킨케어>크림/올인원>페이셜크림 Coupang > 뷰티 > 스킨케어 > 크림/올인원 > 페이셜크림'
3
  • '싸이닉 엔조이 올라운드 워터리 선크림 200g 2개 LotteOn > 뷰티 > 남성화장품 > 선크림 LotteOn > 뷰티 > 남성화장품 > 선크림'
  • ' 상백크림 30ml/50ml SPF50+/PA++++ 1호 크리미 글로우 30ml LotteOn > 뷰티 > 스킨케어 > 크림 LotteOn > 뷰티 > 스킨케어 > 크림'
  • '(485387) 이니스프리 트루히알루론수분선크림기획 SPF50+ PA4+ 50mL+20mL 무료배송 (#M)SSG.COM/스킨케어/스킨케어세트 ssg > 뷰티 > 스킨케어 > 스킨케어세트'

Evaluation

Metrics

Label Accuracy
all 0.3320

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_bt7_test_flat_top_cate")
# Run inference
preds = model("헤라 선 메이트 레포츠 프로 워터프루프 70ml(SPF50+)  (#M)홈>화장품/미용>선케어>선크림 Naverstore > 화장품/미용 > 선케어 > 선크림")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 11 21.836 72
Label Training Sample Count
0 50
1 50
2 50
3 50
4 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
0.0026 1 0.4309 -
0.1279 50 0.4454 -
0.2558 100 0.4001 -
0.3836 150 0.3616 -
0.5115 200 0.3104 -
0.6394 250 0.2446 -
0.7673 300 0.1921 -
0.8951 350 0.1521 -
1.0230 400 0.1177 -
1.1509 450 0.0973 -
1.2788 500 0.0926 -
1.4066 550 0.0866 -
1.5345 600 0.0826 -
1.6624 650 0.078 -
1.7903 700 0.0741 -
1.9182 750 0.0709 -
2.0460 800 0.0658 -
2.1739 850 0.0657 -
2.3018 900 0.0566 -
2.4297 950 0.0549 -
2.5575 1000 0.043 -
2.6854 1050 0.0391 -
2.8133 1100 0.0197 -
2.9412 1150 0.0108 -
3.0691 1200 0.0085 -
3.1969 1250 0.0082 -
3.3248 1300 0.0067 -
3.4527 1350 0.0082 -
3.5806 1400 0.0077 -
3.7084 1450 0.007 -
3.8363 1500 0.0046 -
3.9642 1550 0.0049 -
4.0921 1600 0.0041 -
4.2199 1650 0.003 -
4.3478 1700 0.0003 -
4.4757 1750 0.0002 -
4.6036 1800 0.0 -
4.7315 1850 0.0 -
4.8593 1900 0.0 -
4.9872 1950 0.0 -
5.1151 2000 0.0 -
5.2430 2050 0.0 -
5.3708 2100 0.0 -
5.4987 2150 0.0 -
5.6266 2200 0.0 -
5.7545 2250 0.0001 -
5.8824 2300 0.0001 -
6.0102 2350 0.0 -
6.1381 2400 0.0003 -
6.2660 2450 0.0 -
6.3939 2500 0.0 -
6.5217 2550 0.0002 -
6.6496 2600 0.0007 -
6.7775 2650 0.0008 -
6.9054 2700 0.0028 -
7.0332 2750 0.0024 -
7.1611 2800 0.0002 -
7.2890 2850 0.0 -
7.4169 2900 0.0 -
7.5448 2950 0.0 -
7.6726 3000 0.0 -
7.8005 3050 0.0 -
7.9284 3100 0.0 -
8.0563 3150 0.0001 -
8.1841 3200 0.0 -
8.3120 3250 0.0 -
8.4399 3300 0.0002 -
8.5678 3350 0.0002 -
8.6957 3400 0.0 -
8.8235 3450 0.0002 -
8.9514 3500 0.0 -
9.0793 3550 0.0 -
9.2072 3600 0.0 -
9.3350 3650 0.0 -
9.4629 3700 0.0 -
9.5908 3750 0.0 -
9.7187 3800 0.0 -
9.8465 3850 0.0 -
9.9744 3900 0.0 -
10.1023 3950 0.0 -
10.2302 4000 0.0 -
10.3581 4050 0.0 -
10.4859 4100 0.0 -
10.6138 4150 0.0 -
10.7417 4200 0.0 -
10.8696 4250 0.0 -
10.9974 4300 0.0 -
11.1253 4350 0.0 -
11.2532 4400 0.0 -
11.3811 4450 0.0 -
11.5090 4500 0.0 -
11.6368 4550 0.0 -
11.7647 4600 0.0002 -
11.8926 4650 0.0 -
12.0205 4700 0.0 -
12.1483 4750 0.0 -
12.2762 4800 0.0 -
12.4041 4850 0.0 -
12.5320 4900 0.0 -
12.6598 4950 0.0 -
12.7877 5000 0.0 -
12.9156 5050 0.0 -
13.0435 5100 0.0 -
13.1714 5150 0.0 -
13.2992 5200 0.0 -
13.4271 5250 0.0 -
13.5550 5300 0.0 -
13.6829 5350 0.0 -
13.8107 5400 0.0 -
13.9386 5450 0.0 -
14.0665 5500 0.0 -
14.1944 5550 0.0 -
14.3223 5600 0.0 -
14.4501 5650 0.0 -
14.5780 5700 0.0 -
14.7059 5750 0.0 -
14.8338 5800 0.0005 -
14.9616 5850 0.0 -
15.0895 5900 0.0 -
15.2174 5950 0.0 -
15.3453 6000 0.0 -
15.4731 6050 0.0 -
15.6010 6100 0.0 -
15.7289 6150 0.0 -
15.8568 6200 0.0 -
15.9847 6250 0.0 -
16.1125 6300 0.0 -
16.2404 6350 0.0 -
16.3683 6400 0.0 -
16.4962 6450 0.0 -
16.6240 6500 0.0 -
16.7519 6550 0.0 -
16.8798 6600 0.0 -
17.0077 6650 0.0 -
17.1355 6700 0.0 -
17.2634 6750 0.0 -
17.3913 6800 0.0 -
17.5192 6850 0.0 -
17.6471 6900 0.0 -
17.7749 6950 0.0 -
17.9028 7000 0.0 -
18.0307 7050 0.0 -
18.1586 7100 0.0004 -
18.2864 7150 0.0008 -
18.4143 7200 0.0012 -
18.5422 7250 0.001 -
18.6701 7300 0.0002 -
18.7980 7350 0.0001 -
18.9258 7400 0.0 -
19.0537 7450 0.0 -
19.1816 7500 0.0 -
19.3095 7550 0.0 -
19.4373 7600 0.0 -
19.5652 7650 0.0 -
19.6931 7700 0.0 -
19.8210 7750 0.0 -
19.9488 7800 0.0 -
20.0767 7850 0.0 -
20.2046 7900 0.0003 -
20.3325 7950 0.0 -
20.4604 8000 0.0 -
20.5882 8050 0.0 -
20.7161 8100 0.0 -
20.8440 8150 0.0 -
20.9719 8200 0.0 -
21.0997 8250 0.0 -
21.2276 8300 0.0 -
21.3555 8350 0.0 -
21.4834 8400 0.0 -
21.6113 8450 0.0 -
21.7391 8500 0.0 -
21.8670 8550 0.0 -
21.9949 8600 0.0 -
22.1228 8650 0.0 -
22.2506 8700 0.0 -
22.3785 8750 0.0 -
22.5064 8800 0.0 -
22.6343 8850 0.0 -
22.7621 8900 0.0 -
22.8900 8950 0.0 -
23.0179 9000 0.0 -
23.1458 9050 0.0 -
23.2737 9100 0.0 -
23.4015 9150 0.0 -
23.5294 9200 0.0 -
23.6573 9250 0.0 -
23.7852 9300 0.0 -
23.9130 9350 0.0 -
24.0409 9400 0.0 -
24.1688 9450 0.0 -
24.2967 9500 0.0 -
24.4246 9550 0.0 -
24.5524 9600 0.0 -
24.6803 9650 0.0 -
24.8082 9700 0.0 -
24.9361 9750 0.0 -
25.0639 9800 0.0 -
25.1918 9850 0.0 -
25.3197 9900 0.0 -
25.4476 9950 0.0 -
25.5754 10000 0.0 -
25.7033 10050 0.0 -
25.8312 10100 0.0 -
25.9591 10150 0.0 -
26.0870 10200 0.0 -
26.2148 10250 0.0 -
26.3427 10300 0.0 -
26.4706 10350 0.0 -
26.5985 10400 0.0 -
26.7263 10450 0.0 -
26.8542 10500 0.0 -
26.9821 10550 0.0 -
27.1100 10600 0.0 -
27.2379 10650 0.0 -
27.3657 10700 0.0 -
27.4936 10750 0.0 -
27.6215 10800 0.0 -
27.7494 10850 0.0 -
27.8772 10900 0.0 -
28.0051 10950 0.0 -
28.1330 11000 0.0 -
28.2609 11050 0.0 -
28.3887 11100 0.0 -
28.5166 11150 0.0 -
28.6445 11200 0.0 -
28.7724 11250 0.0 -
28.9003 11300 0.0 -
29.0281 11350 0.0 -
29.1560 11400 0.0 -
29.2839 11450 0.0 -
29.4118 11500 0.0 -
29.5396 11550 0.0 -
29.6675 11600 0.0 -
29.7954 11650 0.0 -
29.9233 11700 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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Evaluation results

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