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mini1013
/
master_cate_bt_top10_test

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

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
6
  • '남자클렌징폼 알로에성분 수분밸런스 세면도구 미셀라 클클 워터 미셀라워터100ml (#M)위메프 > 뷰티 > 이미용소품/기기 > 클렌징소품 > 브러쉬/진동클렌저 위메프 > 뷰티 > 이미용소품/기기 > 클렌징소품 > 브러쉬/진동클렌저'
  • '차앤박 CNP 에이클린 퓨리파잉 포밍 클렌저 145mL LotteOn > 뷰티 > 남성화장품 > 남성화장품세트 LotteOn > 뷰티 > 남성화장품 > 남성화장품세트'
  • '[클린앤드클리어] 딥 액션 블랙헤드 데일리 클렌저 100gx2 CC딥액션블랙헤드클렌저100gx2 (#M)뷰티>화장품/향수>스킨케어>로션/에멀전 CJmall > 뷰티 > 화장품/향수 > 스킨케어 > 에센스/세럼/오일'
2
  • '(키엘) 미드나잇 리커버리 보태니컬 클렌징 오일 - 모든 피부용 --85ml/2.8oz ssg > 뷰티 > 스킨케어 > 스킨/토너/미스트 > 스킨/토너 LOREAL > Ssg > 키엘 > Branded > 키엘'
  • '마녀공장 퓨어 클렌징 오일 141238 200ml x 3개 (#M)11st>바디케어>바디미스트>바디미스트 11st > 뷰티 > 바디케어 > 바디미스트'
  • '[정품 세럼쿠션 & 비타민 크림 샘플 증정] 인텐시브 세럼 파운데이션 세트 쿨 아이보리 ssg > 뷰티 > 메이크업 > 립메이크업;ssg > 뷰티 > 메이크업 > 베이스메이크업;ssg > 뷰티 > 스킨케어 > 스킨/토너;ssg > 뷰티 > 메이크업 > 베이스메이크업 > 파운데이션;SSG.COM/메이크업/베이스메이크업/리퀴드파운데이션;ssg > 뷰티 > 메이크업 > 아이메이크업 > 아이섀도우;ssg > 뷰티 > 명품화장품 > 메이크업 ssg > 뷰티 > 메이크업 > 아이메이크업'
5
  • '[라끄베르] 딥 앤 모이스트 클렌징 티슈 05_클렌징 티슈 80매 홈>5월 행사;홈>6월 행사!;홈>전체상품;(#M)홈>라끄베르 Naverstore > 화장품/미용 > 클렌징 > 클렌징티슈'
  • '토니모리 프로클린 소프트 클렌징 티슈 1+1 (#M)홈>화장품/미용>클렌징>클렌징티슈 Naverstore > 화장품/미용 > 클렌징 > 클렌징티슈'
  • '[소미Pick! 코스알엑스] 원스텝 스킨패드 3종 / NEW 더 비타민C 세럼 外 포어리스 패드 11st>뷰티>스킨케어>스킨/로션;11st>스킨케어>스킨/토너>스킨/토너;11st Hour Event > 패션/뷰티 > 뷰티 > 스킨케어 > 스킨/로션 11st Hour Event > 패션/뷰티 > 뷰티 > 스킨케어 > 스킨/로션'
0
  • '[일리윤] 프레쉬 모이스춰 립앤아이리무버 100ml 3개 단일상품 (#M)위메프 > 뷰티 > 네일케어 > 네일리무버 > 네일리무버 위메프 > 뷰티 > 네일케어 > 네일리무버 > 네일리무버'
  • '키스미 히로인메이크 스피디 마스카라 리무버 6.마스카라 리무버(K407A) (#M)화장품/향수>색조메이크업>마스카라 Gmarket > 뷰티 > 화장품/향수 > 색조메이크업 > 마스카라'
  • '랑콤 비파실 200ml ssg > 뷰티 > 스킨케어 > 스킨/토너 ssg > 뷰티 > 스킨케어 > 스킨/토너'
4
  • '라떼 다 토일레테 250ml 화이트_Free (#M)뷰티>헤어/바디/미용기기>바디케어>바디로션/크림 CJmall > 뷰티 > 화장품/향수 > 향수/홈프래그런스 > 디퓨저/방향제'
  • '마몽드 트리플 멀티 클렌징 크림 190ml (#M)GSSHOP>뷰티>스킨케어>스킨케어세트 GSSHOP > 뷰티 > 스킨케어 > 스킨케어세트'
  • '마몽드 트리플 멀티 클렌징 크림 190ml MinSellAmount (#M)화장품/향수>클렌징/필링>클렌징크림 Gmarket > 뷰티 > 화장품/향수 > 클렌징/필링 > 클렌징크림'
1
  • '클라란스 컴포트 스크럽 - 너리싱 오일 스크럽50ml/1.7oz (#M)홈>스트로베리넷>향수
3
  • '산타마리아노벨라 아쿠아 디 로즈 미셀라 워터 200ml 투명_F (#M)화장품/미용>클렌징>클렌징워터 Naverstore > 화장품/미용 > 클렌징 > 클렌징워터'
  • '[쿠폰+T11%] 라네즈 퍼펙트리뉴 유스 레티놀 프로 꿀잠 잠옷 증정!/1밤1레티놀/라네즈레티놀 35. 라네즈 워터뱅크 아이젤 25ml_선택완료 쇼킹딜 홈>뷰티>스킨케어>스킨/로션;11st>스킨케어>스킨/토너>스킨/토너;쇼킹딜 홈>뷰티>선케어/메이크업>선블록;11st>뷰티>선케어/메이크업>선블록;11st > 뷰티 > 스킨케어 > 스킨/토너;(#M)11st>뷰티>스킨케어>스킨/로션 11st Hour Event > 패션/뷰티 > 뷰티 > 스킨케어 > 스킨/로션'
  • '[클린앤클리어] 미셀라 워터 100ml x2 (#M)GSSHOP>뷰티>클렌징>클렌징폼 GSSHOP > 뷰티 > 클렌징 > 클렌징폼'

Evaluation

Metrics

Label Accuracy
all 0.9232

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_top10_test")
# Run inference
preds = model("프로필링 소프트젤 100ml 피부 세안제 클렌징 필링  (#M)홈>화장품/미용>클렌징>스크럽/필링 Naverstore > 화장품/미용 > 클렌징 > 스크럽/필링")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 11 21.9571 61
Label Training Sample Count
0 50
1 50
2 50
3 50
4 50
5 50
6 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
0.0018 1 0.4528 -
0.0914 50 0.4525 -
0.1828 100 0.4612 -
0.2742 150 0.4424 -
0.3656 200 0.4291 -
0.4570 250 0.3832 -
0.5484 300 0.3246 -
0.6399 350 0.2943 -
0.7313 400 0.2745 -
0.8227 450 0.2655 -
0.9141 500 0.2604 -
1.0055 550 0.253 -
1.0969 600 0.2367 -
1.1883 650 0.228 -
1.2797 700 0.2115 -
1.3711 750 0.1976 -
1.4625 800 0.1786 -
1.5539 850 0.1609 -
1.6453 900 0.1472 -
1.7367 950 0.13 -
1.8282 1000 0.1213 -
1.9196 1050 0.1079 -
2.0110 1100 0.1058 -
2.1024 1150 0.0985 -
2.1938 1200 0.0824 -
2.2852 1250 0.0546 -
2.3766 1300 0.039 -
2.4680 1350 0.0202 -
2.5594 1400 0.0089 -
2.6508 1450 0.0044 -
2.7422 1500 0.004 -
2.8336 1550 0.0045 -
2.9250 1600 0.0016 -
3.0165 1650 0.0005 -
3.1079 1700 0.0004 -
3.1993 1750 0.0003 -
3.2907 1800 0.0002 -
3.3821 1850 0.0002 -
3.4735 1900 0.0001 -
3.5649 1950 0.0002 -
3.6563 2000 0.0003 -
3.7477 2050 0.0002 -
3.8391 2100 0.0001 -
3.9305 2150 0.0001 -
4.0219 2200 0.0002 -
4.1133 2250 0.0002 -
4.2048 2300 0.0003 -
4.2962 2350 0.0001 -
4.3876 2400 0.0003 -
4.4790 2450 0.0001 -
4.5704 2500 0.0001 -
4.6618 2550 0.0006 -
4.7532 2600 0.0002 -
4.8446 2650 0.0001 -
4.9360 2700 0.0022 -
5.0274 2750 0.0046 -
5.1188 2800 0.0028 -
5.2102 2850 0.0033 -
5.3016 2900 0.0025 -
5.3931 2950 0.0023 -
5.4845 3000 0.002 -
5.5759 3050 0.004 -
5.6673 3100 0.0044 -
5.7587 3150 0.004 -
5.8501 3200 0.0027 -
5.9415 3250 0.0032 -
6.0329 3300 0.0002 -
6.1243 3350 0.0003 -
6.2157 3400 0.0 -
6.3071 3450 0.0006 -
6.3985 3500 0.0005 -
6.4899 3550 0.0035 -
6.5814 3600 0.0053 -
6.6728 3650 0.004 -
6.7642 3700 0.0042 -
6.8556 3750 0.0046 -
6.9470 3800 0.0038 -
7.0384 3850 0.0017 -
7.1298 3900 0.0015 -
7.2212 3950 0.0001 -
7.3126 4000 0.0 -
7.4040 4050 0.0 -
7.4954 4100 0.0001 -
7.5868 4150 0.0 -
7.6782 4200 0.0002 -
7.7697 4250 0.0001 -
7.8611 4300 0.0005 -
7.9525 4350 0.0 -
8.0439 4400 0.0002 -
8.1353 4450 0.0 -
8.2267 4500 0.0008 -
8.3181 4550 0.0001 -
8.4095 4600 0.0002 -
8.5009 4650 0.0 -
8.5923 4700 0.0 -
8.6837 4750 0.0 -
8.7751 4800 0.0 -
8.8665 4850 0.0002 -
8.9580 4900 0.0008 -
9.0494 4950 0.0009 -
9.1408 5000 0.0004 -
9.2322 5050 0.0 -
9.3236 5100 0.0 -
9.4150 5150 0.0002 -
9.5064 5200 0.0006 -
9.5978 5250 0.0021 -
9.6892 5300 0.0004 -
9.7806 5350 0.0016 -
9.8720 5400 0.0003 -
9.9634 5450 0.0 -
10.0548 5500 0.0002 -
10.1463 5550 0.0 -
10.2377 5600 0.0 -
10.3291 5650 0.0001 -
10.4205 5700 0.0008 -
10.5119 5750 0.0002 -
10.6033 5800 0.0003 -
10.6947 5850 0.0 -
10.7861 5900 0.0 -
10.8775 5950 0.0 -
10.9689 6000 0.0 -
11.0603 6050 0.0 -
11.1517 6100 0.0 -
11.2431 6150 0.0 -
11.3346 6200 0.0 -
11.4260 6250 0.0 -
11.5174 6300 0.0 -
11.6088 6350 0.0 -
11.7002 6400 0.0 -
11.7916 6450 0.0 -
11.8830 6500 0.0 -
11.9744 6550 0.0 -
12.0658 6600 0.0 -
12.1572 6650 0.0 -
12.2486 6700 0.0 -
12.3400 6750 0.0001 -
12.4314 6800 0.0001 -
12.5229 6850 0.0002 -
12.6143 6900 0.0 -
12.7057 6950 0.0003 -
12.7971 7000 0.0001 -
12.8885 7050 0.0004 -
12.9799 7100 0.0018 -
13.0713 7150 0.0002 -
13.1627 7200 0.0 -
13.2541 7250 0.0001 -
13.3455 7300 0.0 -
13.4369 7350 0.0 -
13.5283 7400 0.0001 -
13.6197 7450 0.0 -
13.7112 7500 0.0 -
13.8026 7550 0.0 -
13.8940 7600 0.0 -
13.9854 7650 0.0 -
14.0768 7700 0.0 -
14.1682 7750 0.0 -
14.2596 7800 0.0 -
14.3510 7850 0.0 -
14.4424 7900 0.0 -
14.5338 7950 0.0 -
14.6252 8000 0.0 -
14.7166 8050 0.0 -
14.8080 8100 0.0 -
14.8995 8150 0.0 -
14.9909 8200 0.0 -
15.0823 8250 0.0 -
15.1737 8300 0.0 -
15.2651 8350 0.0 -
15.3565 8400 0.0 -
15.4479 8450 0.0 -
15.5393 8500 0.0 -
15.6307 8550 0.0 -
15.7221 8600 0.0 -
15.8135 8650 0.0 -
15.9049 8700 0.0 -
15.9963 8750 0.0 -
16.0878 8800 0.0 -
16.1792 8850 0.0 -
16.2706 8900 0.0 -
16.3620 8950 0.0 -
16.4534 9000 0.0 -
16.5448 9050 0.0 -
16.6362 9100 0.0 -
16.7276 9150 0.0 -
16.8190 9200 0.0 -
16.9104 9250 0.0 -
17.0018 9300 0.0 -
17.0932 9350 0.0 -
17.1846 9400 0.0 -
17.2761 9450 0.0 -
17.3675 9500 0.0 -
17.4589 9550 0.0002 -
17.5503 9600 0.0011 -
17.6417 9650 0.0003 -
17.7331 9700 0.0 -
17.8245 9750 0.0001 -
17.9159 9800 0.0001 -
18.0073 9850 0.0002 -
18.0987 9900 0.0003 -
18.1901 9950 0.0 -
18.2815 10000 0.0 -
18.3729 10050 0.0 -
18.4644 10100 0.0 -
18.5558 10150 0.0002 -
18.6472 10200 0.0002 -
18.7386 10250 0.0 -
18.8300 10300 0.0004 -
18.9214 10350 0.0 -
19.0128 10400 0.0 -
19.1042 10450 0.0 -
19.1956 10500 0.0 -
19.2870 10550 0.0 -
19.3784 10600 0.0 -
19.4698 10650 0.0 -
19.5612 10700 0.0 -
19.6527 10750 0.0002 -
19.7441 10800 0.0003 -
19.8355 10850 0.0 -
19.9269 10900 0.0 -
20.0183 10950 0.0 -
20.1097 11000 0.0 -
20.2011 11050 0.0 -
20.2925 11100 0.0 -
20.3839 11150 0.0 -
20.4753 11200 0.0 -
20.5667 11250 0.0 -
20.6581 11300 0.0 -
20.7495 11350 0.0 -
20.8410 11400 0.0 -
20.9324 11450 0.0 -
21.0238 11500 0.0004 -
21.1152 11550 0.0 -
21.2066 11600 0.0 -
21.2980 11650 0.0 -
21.3894 11700 0.0 -
21.4808 11750 0.0 -
21.5722 11800 0.0 -
21.6636 11850 0.0 -
21.7550 11900 0.0 -
21.8464 11950 0.0 -
21.9378 12000 0.0 -
22.0293 12050 0.0 -
22.1207 12100 0.0 -
22.2121 12150 0.0 -
22.3035 12200 0.0 -
22.3949 12250 0.0 -
22.4863 12300 0.0 -
22.5777 12350 0.0 -
22.6691 12400 0.0 -
22.7605 12450 0.0 -
22.8519 12500 0.0 -
22.9433 12550 0.0001 -
23.0347 12600 0.0 -
23.1261 12650 0.0 -
23.2176 12700 0.0 -
23.3090 12750 0.0 -
23.4004 12800 0.0 -
23.4918 12850 0.0 -
23.5832 12900 0.0 -
23.6746 12950 0.0 -
23.7660 13000 0.0 -
23.8574 13050 0.0 -
23.9488 13100 0.0 -
24.0402 13150 0.0 -
24.1316 13200 0.0 -
24.2230 13250 0.0 -
24.3144 13300 0.0 -
24.4059 13350 0.0 -
24.4973 13400 0.0 -
24.5887 13450 0.0 -
24.6801 13500 0.0001 -
24.7715 13550 0.0 -
24.8629 13600 0.0 -
24.9543 13650 0.0 -
25.0457 13700 0.0 -
25.1371 13750 0.0 -
25.2285 13800 0.0 -
25.3199 13850 0.0 -
25.4113 13900 0.0 -
25.5027 13950 0.0 -
25.5941 14000 0.0 -
25.6856 14050 0.0 -
25.7770 14100 0.0 -
25.8684 14150 0.0 -
25.9598 14200 0.0 -
26.0512 14250 0.0 -
26.1426 14300 0.0 -
26.2340 14350 0.0 -
26.3254 14400 0.0 -
26.4168 14450 0.0 -
26.5082 14500 0.0 -
26.5996 14550 0.0 -
26.6910 14600 0.0 -
26.7824 14650 0.0 -
26.8739 14700 0.0 -
26.9653 14750 0.0 -
27.0567 14800 0.0 -
27.1481 14850 0.0 -
27.2395 14900 0.0 -
27.3309 14950 0.0 -
27.4223 15000 0.0 -
27.5137 15050 0.0 -
27.6051 15100 0.0 -
27.6965 15150 0.0 -
27.7879 15200 0.0 -
27.8793 15250 0.0 -
27.9707 15300 0.0 -
28.0622 15350 0.0 -
28.1536 15400 0.0 -
28.2450 15450 0.0 -
28.3364 15500 0.0 -
28.4278 15550 0.0 -
28.5192 15600 0.0 -
28.6106 15650 0.0 -
28.7020 15700 0.0 -
28.7934 15750 0.0 -
28.8848 15800 0.0 -
28.9762 15850 0.0 -
29.0676 15900 0.0 -
29.1590 15950 0.0 -
29.2505 16000 0.0 -
29.3419 16050 0.0 -
29.4333 16100 0.0 -
29.5247 16150 0.0 -
29.6161 16200 0.0 -
29.7075 16250 0.0 -
29.7989 16300 0.0 -
29.8903 16350 0.0 -
29.9817 16400 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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