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mini1013
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master_cate_top_bt5_4

Text Classification
setfit
Safetensors
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results
Model card Files Community

SetFit with klue/roberta-base

This is a SetFit model that can be used for Text Classification. This SetFit model uses klue/roberta-base 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 Type: SetFit
  • Sentence Transformer body: klue/roberta-base
  • Classification head: a LogisticRegression instance
  • Maximum Sequence Length: 512 tokens
  • Number of Classes: 5 classes

Model Sources

Model Labels

Label Examples
2
  • '에스쁘아 프로테일러 비글로우 스틱 파운데이션 13g 23호베이지 (#M)홈>화장품/미용>베이스메이크업>파운데이션>스틱형 Naverstore > 화장품/미용 > 베이스메이크업 > 파운데이션 > 스틱형'
  • '그라펜 에어커버 스틱 파운데이션 23호 베이지 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 파운데이션 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 파운데이션'
  • '바비 브라운 스킨 파운데이션 스틱-2.5 원 샌드 9g (#M)화장품/미용>베이스메이크업>파운데이션>크림형 Naverstore > 화장품/미용 > 베이스메이크업 > 파운데이션 > 크림형'
1
  • '정샘물 스킨 세팅 톤 코렉팅 베이스 40ml 글로잉 베이스 (#M)11st>메이크업>페이스메이크업>메이크업베이스 11st > 뷰티 > 메이크업 > 페이스메이크업 > 메이크업베이스'
  • '아이오페 퍼펙트 커버 메이크업베이스 35ml 2호 라이트퍼플 × 3개 (#M)쿠팡 홈>뷰티>메이크업>베이스 메이크업>베이스/프라이머 Coupang > 뷰티 > 메이크업 > 베이스 메이크업 > 베이스/프라이머'
  • '아이오페 퍼펙트 커버 베이스 35ml 2호-퍼플 (#M)홈>화장품/미용>베이스메이크업>메이크업베이스 Naverstore > 화장품/미용 > 베이스메이크업 > 메이크업베이스'
0
  • '헤라 글로우 래스팅 파운데이션 17C1 페탈 아이보리 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 베이스/프라이머 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 베이스/프라이머'
  • '[에스티 로더] 더블웨어 파운데이션 30ml SPF 10/PA++ (+프라이머 정품 ) 1W0 웜 포슬린 홈>기획 세트;홈>더블웨어;홈>더블 웨어;화장품/미용>베이스메이크업>파운데이션>리퀴드형;(#M)홈>전체상품 Naverstore > 베이스메이크업 > 파운데이션'
  • '에스쁘아 프로테일러 파운데이션 비 글로우 10ml 4호 베이지 × 1개 (#M)쿠팡 홈>뷰티>메이크업>베이스 메이크업>파운데이션 Coupang > 뷰티 > 로드샵 > 메이크업 > 베이스 메이크업 > 파운데이션'
4
  • '시세이도 스포츠 커버 파운데이션 20g S101 (#M)홈>화장품/미용>베이스메이크업>파운데이션>크림형 Naverstore > 화장품/미용 > 베이스메이크업 > 파운데이션 > 크림형'
  • '시세이도 스포츠 커버 파운데이션 20g S100 × 1개 Coupang > 뷰티 > 메이크업 > 베이스 메이크업 > 파운데이션;(#M)쿠팡 홈>뷰티>메이크업>베이스 메이크업>파운데이션 Coupang > 뷰티 > 메이크업 > 베이스 메이크업 > 파운데이션'
  • '에이지투웨니스 오리지날 샤이닝드롭 케이스+리필3개 (+커피쿠폰+폼20ml) 샤이닝드롭(화이트)23호케이스+리필3개_폼20ml (#M)화장품/미용>베이스메이크업>파운데이션>쿠션형 AD > Naverstore > 화장품/미용 > 베이스메이크업 > 파운데이션 > 크림형'
3
  • '매트 벨벳 스킨 컴팩트 스폰지 단품없음 LotteOn > 뷰티 > 뷰티기기 > 액세서리/소모품 LotteOn > 뷰티 > 뷰티기기 > 액세서리/소모품'
  • '[BF적립] 엉크르 드 뽀 쿠션&리필 세트(+스탠딩 미러+5천LPOINT) 20호_15호 LOREAL > DepartmentLotteOn > 입생로랑 > Branded > 입생로랑 LOREAL > DepartmentLotteOn > 입생로랑 > Branded > 입생로랑'
  • '코튼 LotteOn > 뷰티 > 뷰티기기 > 액세서리/소모품 LotteOn > 뷰티 > 뷰티기기 > 액세서리/소모품'

Evaluation

Metrics

Label Accuracy
all 0.9475

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")
# Run inference
preds = model("[시세이도] NEW 싱크로 스킨 래디언트 리프팅 파운데이션 SPF30/PA++++ 30ml 130 오팔 (#M)홈>메이크업>베이스메이크업 HMALL > 뷰티 > 메이크업 > 베이스메이크업")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 12 22.928 52
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 0.521 -
0.1279 50 0.4636 -
0.2558 100 0.42 -
0.3836 150 0.292 -
0.5115 200 0.1539 -
0.6394 250 0.0626 -
0.7673 300 0.0343 -
0.8951 350 0.0071 -
1.0230 400 0.0023 -
1.1509 450 0.0005 -
1.2788 500 0.0006 -
1.4066 550 0.0003 -
1.5345 600 0.0002 -
1.6624 650 0.0001 -
1.7903 700 0.0002 -
1.9182 750 0.0006 -
2.0460 800 0.0002 -
2.1739 850 0.0001 -
2.3018 900 0.0 -
2.4297 950 0.0 -
2.5575 1000 0.0 -
2.6854 1050 0.0 -
2.8133 1100 0.0 -
2.9412 1150 0.0 -
3.0691 1200 0.0 -
3.1969 1250 0.0 -
3.3248 1300 0.0 -
3.4527 1350 0.0007 -
3.5806 1400 0.0005 -
3.7084 1450 0.0009 -
3.8363 1500 0.0008 -
3.9642 1550 0.0003 -
4.0921 1600 0.0002 -
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 0.0 -
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 0.0 -
5.8824 2300 0.0 -
6.0102 2350 0.0001 -
6.1381 2400 0.0006 -
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 0.0 -
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 0.0 -
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 0.0004 -
19.4373 7600 0.0028 -
19.5652 7650 0.0003 -
19.6931 7700 0.0002 -
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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