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master_cate_top_bt5_4_test_flat

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
  • '랑콤 뗑 이돌 울트라 웨어 스틱 SF 15 No. 04 Beige N 216699 LotteOn > 뷰티 > 베이스메이크업 > 파우더 LotteOn > 뷰티 > 베이스메이크업 > 파우더'
  • '조성아 슈퍼핏 메가프루프 스틱파데 1+1+에센스 미스트 100mlx1 21호 (#M)위메프 > 뷰티 > 메이크업 > 베이스 메이크업 > 파운데이션 위메프 > 뷰티 > 메이크업 > 베이스 메이크업 > 파운데이션'
  • '에스쁘아 가을 메이크업 빅세일 ~50% / 신상 스틱 파운데이션, 밋츠 그레이 립스틱, 룩북 팔레트 등 총 출동 비글로우스틱파데(아이보리)_611123517 ssg > 뷰티 > 메이크업 > 아이메이크업 > 아이섀도우 ssg > 뷰티 > 메이크업 > 아이메이크업 > 아이섀도우'
1
  • '[백화점정품/당일출고] 맥 스트롭 크림 50ml/핑크라이트(오리지널) (#M)홈>화장품/미용>베이스메이크업>메이크업베이스 Naverstore > 화장품/미용 > 베이스메이크업 > 메이크업베이스'
  • '에브리데이 IOPE 퍼펙트 커버 베이스35ml 메이크업 컨실러 비비 13579EA ◎＆쿠팡 본상품선택 × ◎＆쿠팡 2호 라이트퍼플 (#M)쿠팡 홈>뷰티>메이크업>베이스 메이크업>베이스 메이크업 세트 Coupang > 뷰티 > 메이크업 > 베이스 메이크업 > 베이스 메이크업 세트'
  • '맥 스트롭 크림 50ml 실버라이트 (#M)위메프 > 뷰티 > 남성화장품 > 남성 메이크업 > 남성 베이스메이크업 위메프 > 뷰티 > 남성화장품 > 남성 메이크업 > 남성 베이스메이크업'
0
  • '[본사직영][기획]NEW 헤라 실키 스테이 파운데이션+파운데이션 브러쉬(48000원상당 본품동일사양)+블러셔 21W1 (#M)홈>메이크업>베이스메이크업 HMALL > 뷰티 > 메이크업 > 베이스메이크업'
  • 'CHANEL 레 베쥬 뚜쉬 드 뗑 BR12 (#M)홈>화장품/미용>베이스메이크업>파운데이션>리퀴드형 Naverstore > 화장품/미용 > 베이스메이크업 > 파운데이션 > 리퀴드형'
  • '에스티로더 [단독] 더블웨어 파운데이션 세트 (+마스카라 정품 ) 226792 1C0 쉘 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 파운데이션 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 파운데이션'
4
  • '에스티 로더 더블 웨어 spf 10 - 36 샌드 30ml LotteOn > 뷰티 > 베이스메이크업 > 파운데이션 LotteOn > 뷰티 > 베이스메이크업 > 파운데이션'
  • 'Maybelline New York Maybelline New York Dream Smooth Mousse Foundation, Pure Beige, 0.49 Ounce LotteOn > 뷰티 > 색조메이크업 > 색조메이크업세트 LotteOn > 뷰티 > 색조메이크업 > 색조메이크업세트'
  • '수블리마지 르 뗑 10 베쥬 LotteOn > 뷰티 > 베이스메이크업 > 파운데이션 LotteOn > 뷰티 > 베이스메이크업 > 파운데이션'
3
  • '[포렌코즈] 필 워터 쿠션 상세 설명 참조_피부타입:23호 내추럴베이지 (#M)쿠팡 홈>뷰티>메이크업>베이스 메이크업>파운데이션 Coupang > 뷰티 > 메이크업 > 베이스 메이크업 > 파운데이션'
  • '[현대백화점][시세이도]UV 프로텍티브 컴팩트 파운데이션 본품 SPF35/PA+++ [00004] 미디엄 아이보리 홈>화장품/미용>베이스메이크업>파운데이션>쿠션형;홈>화장품/미용>선케어>선파우더/쿠션;(#M)홈>화장품/미용>베이스메이크업>파운데이션>리퀴드형 Naverstore > 화장품/미용 > 선케어 > 선파우더/쿠션'
  • '롬앤 제로쿠션 spf20pa++ 0g발림성 세미매트쿠션 내추럴21호본품 화장품/향수>에어쿠션/팩트>에어쿠션;(#M)화장품/향수>베이스메이크업>파우더/트윈케이크 Gmarket > 뷰티 > 화장품/향수 > 베이스메이크업 > 파우더/트윈케이크'

Evaluation

Metrics

Label Accuracy
all 0.9587

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_top_bt5_4_test_flat")
# Run inference
preds = model("입생로랑 뚜쉬 에끌라 글로우-팩트 쿠션 B20  LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 쿠션/팩트 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 쿠션/팩트")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 10 23.036 53
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 0.3977 -
0.1279 50 0.4192 -
0.2558 100 0.3842 -
0.3836 150 0.3421 -
0.5115 200 0.3111 -
0.6394 250 0.2705 -
0.7673 300 0.2216 -
0.8951 350 0.1494 -
1.0230 400 0.0875 -
1.1509 450 0.0521 -
1.2788 500 0.0321 -
1.4066 550 0.004 -
1.5345 600 0.0019 -
1.6624 650 0.0011 -
1.7903 700 0.0005 -
1.9182 750 0.0004 -
2.0460 800 0.0004 -
2.1739 850 0.0003 -
2.3018 900 0.0003 -
2.4297 950 0.0003 -
2.5575 1000 0.0001 -
2.6854 1050 0.0003 -
2.8133 1100 0.0005 -
2.9412 1150 0.0003 -
3.0691 1200 0.0001 -
3.1969 1250 0.0001 -
3.3248 1300 0.0 -
3.4527 1350 0.0001 -
3.5806 1400 0.0 -
3.7084 1450 0.0 -
3.8363 1500 0.0 -
3.9642 1550 0.0 -
4.0921 1600 0.0 -
4.2199 1650 0.0 -
4.3478 1700 0.0 -
4.4757 1750 0.0 -
4.6036 1800 0.0 -
4.7315 1850 0.0 -
4.8593 1900 0.0 -
4.9872 1950 0.0002 -
5.1151 2000 0.0018 -
5.2430 2050 0.0009 -
5.3708 2100 0.0003 -
5.4987 2150 0.0 -
5.6266 2200 0.0002 -
5.7545 2250 0.0 -
5.8824 2300 0.0002 -
6.0102 2350 0.0 -
6.1381 2400 0.0007 -
6.2660 2450 0.0 -
6.3939 2500 0.0 -
6.5217 2550 0.0 -
6.6496 2600 0.0 -
6.7775 2650 0.0 -
6.9054 2700 0.0 -
7.0332 2750 0.0 -
7.1611 2800 0.0 -
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 0.0 -
8.1841 3200 0.0 -
8.3120 3250 0.0 -
8.4399 3300 0.0 -
8.5678 3350 0.0 -
8.6957 3400 0.0 -
8.8235 3450 0.0 -
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 0.0029 -
10.1023 3950 0.0026 -
10.2302 4000 0.0037 -
10.3581 4050 0.0001 -
10.4859 4100 0.0 -
10.6138 4150 0.0003 -
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 0.0 -
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 0.0 -
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 0.0 -
18.2864 7150 0.0 -
18.4143 7200 0.0 -
18.5422 7250 0.0 -
18.6701 7300 0.0 -
18.7980 7350 0.0 -
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 0.0 -
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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