mini1013/master_item_top_bt2

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

• Repository: SetFit on GitHub
• Paper: Efficient Few-Shot Learning Without Prompts
• Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

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}
}