Hugging Face's logo

mini1013
/
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}
}
Downloads last month
10,182
Safetensors
Model size
111M params
Tensor type
F32
·
Inference Examples
Text Classification
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for mini1013/master_item_top_bt2

Base model

klue/roberta-base
Finetuned
(159)
this model

Evaluation results

View on Papers With Code