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:

Fine-tuning a Sentence Transformer with contrastive learning.
Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

Model Type: SetFit
Sentence Transformer body: mini1013/master_domain
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	'[묶음할인~25%+T11%]에뛰드 타임어택 ~60% 전품목 빅세일/호랑이의 해 무직타이거 콜라보 런칭 50.패치(1)_매끈반짝3단코팩5개_650000010398 쇼킹딜 홈>뷰티>선케어/메이크업>아이메이크업;11st>메이크업>아이메이크업>아이섀도우;11st>뷰티>선케어/메이크업>아이메이크업;11st Hour Event > 패션/뷰티 > 뷰티 > 선케어/메이크업 > 아이메이크업 11st Hour Event > 패션/뷰티 > 뷰티 > 선케어/메이크업 > 아이메이크업' '차앤박 안티포어 블랙헤드 클리어 키트 스트립 (#M)홈>화장품/미용>마스크/팩>코팩 Naverstore > 화장품/미용 > 마스크/팩 > 코팩' '[차앤박] CNP 안티포어 블랙헤드 클리어 키트 스트립 3세트(3회분) (#M)위메프 > 뷰티 > 스킨케어 > 팩/마스크 > 코팩 위메프 > 뷰티 > 스킨케어 > 팩/마스크 > 코팩'
0	'[10%+15%]한스킨 6월 클리어런스 클렌징오일/토너패드/에센스/블랙헤드/마스크~81%OF 블레미쉬 커버 컨실러_브라이트 [GH990355] 쇼킹딜 홈>뷰티>선케어/메이크업>페이스메이크업;11st>뷰티>선케어/메이크업>페이스메이크업;11st>메이크업>페이스메이크업>BB크림;11st > 뷰티 > 메이크업 > 페이스메이크업 11st Hour Event > 패션/뷰티 > 뷰티 > 선케어/메이크업 > 페이스메이크업' '네이처리퍼블릭 [네이처리퍼블릭][1+1]수딩 앤 모이스처 알로에베라 수딩젤 마스크시트 단일옵션 × 선택완료 쿠팡 홈>뷰티>스킨케어>마스크/팩>코팩/기타패치>기타패치;Coupang > 뷰티 > 로드샵 > 스킨케어 > 마스크/팩 > 코팩/기타패치 > 기타패치;(#M)쿠팡 홈>뷰티>스킨케어>마스크/팩>패치/코팩>기타패치 Coupang > 뷰티 > 스킨케어 > 마스크/팩 > 패치/코팩 > 기타패치' '이니스프리 블랙티 유스 인핸싱 앰플 마스크 28ml 1개입 × 5개 LotteOn > 뷰티 > 스킨케어 > 마스크/팩 > 마스크팩 LotteOn > 뷰티 > 스킨케어 > 마스크/팩 > 마스크팩'
2	'[쿠폰30%+스토어10%]에뛰드 ~64% 21년 신제품 앵콜전(플레이컬러아이즈/그림자쉐딩/픽싱틴트/순정) 58.AC 클린업_핑크마스크_111080503 쇼킹딜 홈>뷰티>스킨케어>크림;쇼킹딜 홈>뷰티>스킨케어>스킨/로션;11st>스킨케어>스킨/토너>스킨/토너;11st>메이크업>아이메이크업>아이섀도우;쇼킹딜 홈>뷰티>선케어/메이크업>아이메이크업;11st>뷰티>선케어/메이크업>아이메이크업;11st > timedeal 11st Hour Event > 패션/뷰티 > 뷰티 > 선케어/메이크업 > 아이메이크업' '마스크 오브 매그너민티 315g 파워 마스크 (#M)뷰티>헤어/바디/미용기기>헤어케어>기획세트 CJmall > 뷰티 > 헤어/바디/미용기기 > 헤어스타일링 > 왁스/스프레이' '[말썽피부케어추천] 쑥뜸팩+쑥카밍젤 (#M)위메프 > 뷰티 > 클렌징/필링 > 필링젤/스크럽 > 필링젤/스크럽 위메프 > 뷰티 > 클렌징/필링 > 필링젤/스크럽 > 필링젤/스크럽'
1	'티르티르 물광 콜라겐 生생크림 버블팩 물광마스크 노워시 80ml 당일출고 티르티르콜라겐80ml (#M)홈>화장품/미용>스킨케어>크림 Naverstore > 화장품/미용 > 스킨케어 > 크림' "달바 모델 한혜진's pick 화이트트러플 세럼 7통+아이크림1통 단일상품 TV쇼핑>TV쇼핑 화장품/이미용>화장품/향수>기초스킨케어;(#M)TV상품>TV쇼핑 화장품/이미용>화장품/향수>기초스킨케어 CJmall > 뷰티 > 화장품/향수 > 더모코스메틱 > 에센스/세럼/오일" '시슬리 벨벳 슬리핑 마스크 LotteOn > 뷰티 > 남성화장품 > 남성화장품세트 LotteOn > 뷰티 > 남성화장품 > 남성화장품세트'

Evaluation

Metrics

Label	Accuracy
all	0.5683

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_top3_test")
# Run inference
preds = model("메디힐 티트리 케어솔루션 에센셜 마스크 이엑스  LotteOn > 뷰티 > 마스크/팩 > 마스크팩 LotteOn > 뷰티 > 마스크/팩 > 마스크팩")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	12	22.655	91

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	0.478	-
0.1597	50	0.4392	-
0.3195	100	0.4128	-
0.4792	150	0.3767	-
0.6390	200	0.3406	-
0.7987	250	0.2889	-
0.9585	300	0.2482	-
1.1182	350	0.2336	-
1.2780	400	0.1948	-
1.4377	450	0.1284	-
1.5974	500	0.0958	-
1.7572	550	0.0893	-
1.9169	600	0.0788	-
2.0767	650	0.0706	-
2.2364	700	0.058	-
2.3962	750	0.0476	-
2.5559	800	0.0406	-
2.7157	850	0.0327	-
2.8754	900	0.0198	-
3.0351	950	0.0183	-
3.1949	1000	0.0131	-
3.3546	1050	0.0093	-
3.5144	1100	0.005	-
3.6741	1150	0.0004	-
3.8339	1200	0.0001	-
3.9936	1250	0.0001	-
4.1534	1300	0.0	-
4.3131	1350	0.0001	-
4.4728	1400	0.0	-
4.6326	1450	0.0	-
4.7923	1500	0.0	-
4.9521	1550	0.0	-
5.1118	1600	0.0	-
5.2716	1650	0.0006	-
5.4313	1700	0.0001	-
5.5911	1750	0.0	-
5.7508	1800	0.0	-
5.9105	1850	0.0	-
6.0703	1900	0.0	-
6.2300	1950	0.0	-
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	0.0002	-
8.4665	2650	0.0	-
8.6262	2700	0.0	-
8.7859	2750	0.0001	-
8.9457	2800	0.0	-
9.1054	2850	0.0	-
9.2652	2900	0.0	-
9.4249	2950	0.0002	-
9.5847	3000	0.0096	-
9.7444	3050	0.0007	-
9.9042	3100	0.0006	-
10.0639	3150	0.0005	-
10.2236	3200	0.0001	-
10.3834	3250	0.0018	-
10.5431	3300	0.0003	-
10.7029	3350	0.0003	-
10.8626	3400	0.0	-
11.0224	3450	0.0016	-
11.1821	3500	0.0058	-
11.3419	3550	0.0055	-
11.5016	3600	0.005	-
11.6613	3650	0.0062	-
11.8211	3700	0.0017	-
11.9808	3750	0.0002	-
12.1406	3800	0.0001	-
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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Safetensors

Model size

111M params

Tensor type

F32

Model tree for mini1013/master_cate_bt_top3_test

Base model

klue/roberta-base

Finetuned

mini1013/master_domain

Finetuned

(214)

this model

Evaluation results

Accuracy on Unknown
test set self-reported

0.568

View on Papers With Code