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mini1013
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master_item_top_bt2

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: 4 classes

Model Sources

Model Labels

Label Examples
3
  • '네일아트 네일 연장팁 8종 네일아트팁 네일연장 간편한네일아트 말랑네일팁 손톱연장 다이아몬드팁 (#M)SSG.COM/메이크업/메이크업세트 ssg > 뷰티 > 메이크업 > 메이크업세트'
  • '데싱디바 매직프레스 베르사유(OL) 2209 데싱디바 매직프레스 베르사유(OL) 2209 (#M)홈>네일>팁/스티커>네일 팁 OLIVEYOUNG > 네일 > 팁/스티커 > 네일 팁'
  • '나인펫홈앤리빙 네일팁 세트 31종 테이프글루 포함 NT-006 (#M)SSG.COM/메이크업/네일/네일케어용품 ssg > 뷰티 > 메이크업 > 네일'
0
  • '[OPI][리무버] 넌아세톤리무버 450ml ssg > 뷰티 > 메이크업 > 네일 ssg > 뷰티 > 메이크업 > 네일'
  • '포먼트 젤네일 O.3 라이트 살몬 × 1개 (#M)쿠팡 홈>뷰티>네일>젤네일>컬러 젤 Coupang > 뷰티 > 네일 > 젤네일 > 컬러 젤'
  • '미스터그린 스텐 큐티클 손톱깎이 MR1127 혼합색상 × 1개 (#M)쿠팡 홈>뷰티>네일>네일케어도구>클리퍼/푸셔/니퍼>클리퍼/손톱깎이 Coupang > 뷰티 > 네일 > 네일케어도구 > 클리퍼/푸셔/니퍼 > 클리퍼/손톱깎이'
2
  • '고양이 네일 케어 세트 족집게 + 손톱깎이 + 손톱줄 × 1세트 LotteOn > 뷰티 > 네일 > 네일케어소품 LotteOn > 뷰티 > 네일 > 네일케어소품'
  • '오피아이 OPI [세트상품] 핸드 큐티클 오일 TO GO2810443 2 (#M)SSG.COM/메이크업/네일/네일팁/네일스티커 ssg > 뷰티 > 메이크업 > 네일'
  • '네일샵 네일 패디케어 리클라이너 탁자 의자 스툴세트 전동리클라이닝173도+데크+대형스툴 LotteOn > 뷰티 > 뷰티기기 > 네일관리 LotteOn > 뷰티 > 뷰티기기 > 네일관리'
1
  • '뿌띠슈 컬러팡팡 네일 C13 뿌띠슈의요술봉 8ml × 1개 쿠팡 홈>어린이날>어린이화장품>네일케어;(#M)쿠팡 홈>뷰티>어린이화장품>네일케어 Coupang > 뷰티 > 어린이화장품 > 네일케어'
  • '에크레아)스킨핏브라탑 LARGE_BLACK SSG.COM/스포츠패션/용품/여성스포츠의류/트레이닝복상의;(#M)SSG.COM/헬스/요가/격투기/요가/필라테스 의류/요가복 상의 LOREAL > Ssg > 헬레나 루빈스타인 > Generic > 스킨'
  • '오피아이 네일 락커 매니큐어 15ml 옐로우(A65) × 1개 LotteOn > 뷰티 > 네일 > 네일스티커/네일팁 LotteOn > 뷰티 > 네일 > 네일스티커/네일팁'

Evaluation

Metrics

Label Accuracy
all 0.8434

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_item_top_bt2")
# Run inference
preds = model("네일스케치 2IN1 접착 홀로그램 필름 네일스티커 스톤 × 1개 LotteOn > 뷰티 > 네일 > 네일케어소품 LotteOn > 뷰티 > 네일 > 네일케어소품")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 13 23.395 44
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 0.411 -
0.1597 50 0.3995 -
0.3195 100 0.3502 -
0.4792 150 0.2877 -
0.6390 200 0.2325 -
0.7987 250 0.1729 -
0.9585 300 0.0879 -
1.1182 350 0.066 -
1.2780 400 0.0185 -
1.4377 450 0.0005 -
1.5974 500 0.0002 -
1.7572 550 0.0004 -
1.9169 600 0.0001 -
2.0767 650 0.0001 -
2.2364 700 0.0 -
2.3962 750 0.0002 -
2.5559 800 0.0001 -
2.7157 850 0.0003 -
2.8754 900 0.0001 -
3.0351 950 0.0 -
3.1949 1000 0.0001 -
3.3546 1050 0.0005 -
3.5144 1100 0.0005 -
3.6741 1150 0.0003 -
3.8339 1200 0.0002 -
3.9936 1250 0.0 -
4.1534 1300 0.0 -
4.3131 1350 0.0 -
4.4728 1400 0.0001 -
4.6326 1450 0.0004 -
4.7923 1500 0.0007 -
4.9521 1550 0.0001 -
5.1118 1600 0.0 -
5.2716 1650 0.0 -
5.4313 1700 0.0 -
5.5911 1750 0.0 -
5.7508 1800 0.0 -
5.9105 1850 0.0 -
6.0703 1900 0.0001 -
6.2300 1950 0.0001 -
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 0.0 -
8.4665 2650 0.0 -
8.6262 2700 0.0 -
8.7859 2750 0.0 -
8.9457 2800 0.0 -
9.1054 2850 0.0 -
9.2652 2900 0.0 -
9.4249 2950 0.0 -
9.5847 3000 0.0 -
9.7444 3050 0.0 -
9.9042 3100 0.0 -
10.0639 3150 0.0 -
10.2236 3200 0.0 -
10.3834 3250 0.0 -
10.5431 3300 0.0 -
10.7029 3350 0.0 -
10.8626 3400 0.0 -
11.0224 3450 0.0 -
11.1821 3500 0.0 -
11.3419 3550 0.0 -
11.5016 3600 0.0 -
11.6613 3650 0.0 -
11.8211 3700 0.0 -
11.9808 3750 0.0 -
12.1406 3800 0.0 -
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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