mini1013/master_cate_bt6_test_flat_top_cate

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 Type: SetFit
• Sentence Transformer body: mini1013/master_domain
• Classification head: a LogisticRegression instance
• Maximum Sequence Length: 512 tokens
• Number of Classes: 11 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
0	'[쿠폰10%+~25%묶음]에뛰드 타임어택 50% 반값 전품목 특가 (순정/수분가득콜라겐/섀도우팔레트/클렌징폼) 85.디어달링워터젤틴트_4호앵두레드_650011600 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' '네이처카인드 강력한 헤어커버 타투틴트 1개 (#M)쿠팡 홈>생활용품>헤어/바디/세안>염색/파마>염색/헤어컬러>일반염색제(새치) Coupang > 뷰티 > 헤어 > 염색/파마 > 염색/헤어컬러 > 일반염색제(새치)' '조르지오 아르마니 엑스터 래커 500 빈티지 500 빈티지 (#M)홈>▶▶ 메이크업 Naverstore > 화장품/미용 > 색조메이크업 > 립글로스'
4	'(할인UP)재생소재를 사용한 대표상품+최대38%혜택까지 카.(192-232)가을메이크업은이니스프리_스키니꼼꼼카라ZERO2브라운X2 화장품/향수>스킨/로션>스킨/토너;(#M)화장품/향수>스킨케어>스킨/토너 Gmarket > 뷰티 > 화장품/향수 > 스킨케어' '썸츄어스 레벨 렝쓰 + 리프트 마스카라 DepartmentSsg > 명품화장품 > 메이크업 > 아이 메이크업 > 마스카라 DepartmentSsg > 명품화장품 > 메이크업 > 아이 메이크업 > 마스카라' '더블니즈 팡팡 마스카라 (EM01012600)02 컬링팡 (#M)화장품/향수>색조메이크업>마스카라 Gmarket > 뷰티 > 화장품/향수 > 색조메이크업 > 마스카라'
8	'맨즈 아이브로우 펜슬 흙갈색 (#M)11st>남성화장품>남성BB크림>남성BB크림 11st > 뷰티 > 남성화장품 > 남성BB크림' 'NEW 내추럴 브라우 쉐이퍼 에스프레소 LotteOn > 뷰티 > 네일 > 네일케어 > 큐티클케어 LotteOn > 뷰티 > 네일 > 네일케어 > 큐티클케어' '[하프클럽/메이블린뉴욕]디파인 블렌드 아이브로우 펜슬 0.16g 라이트브라운/상세설명참조 LotteOn > 뷰티 > 클렌징 > 립아이리무버 LotteOn > 뷰티 > 클렌징 > 립아이리무버'
9	'[~묶음20%] 에어리벨벳 물복딱복 출시기념 신상&베스트 모음 027.프로 아이 팔레트 미니_001모노무드_선택사항없음 11st>메이크업>립메이크업>립틴트;쇼킹딜 홈>뷰티>선케어/메이크업>립/치크메이크업;11st>뷰티>선케어/메이크업>립/치크메이크업;11st Hour Event > 패션/뷰티 > 뷰티 > 선케어/메이크업 > 립/치크메이크업 11st Hour Event > 패션/뷰티 > 뷰티 > 선케어/메이크업 > 립/치크메이크업' '[30%+T11%] 에이지투웨니스 샤이닝드롭팩트 케이스+리필3개+단독증정 외 NEW & BEST 모음/루나/포인트 앤 (위글위글)루나 데일리 크러쉬 아이섀도우 팔레트_2호 드라이로즈 쇼킹딜 홈>뷰티>선케어/메이크업>페이스메이크업;11st>뷰티>선케어/메이크업>페이스메이크업;11st>메이크업>페이스메이크업>쿠션팩트;11st > 뷰티 > 메이크업 > 페이스메이크업 11st Hour Event > 패션/뷰티 > 뷰티 > 선케어/메이크업 > 페이스메이크업' '[홀리카홀리카] 즉시10%+금액별쿠폰+T11% 개강맞이 ALL세일 /단독 /1+1/금액별 / 56.하드커버 컴플리트 컨실팔레트_없음 11st>메이크업>아이메이크업>아이섀도우;쇼킹딜 홈>뷰티>선케어/메이크업>아이메이크업;11st>뷰티>선케어/메이크업>아이메이크업;11st > 뷰티 > 메이크업 > 아이메이크업 > 아이섀도우 11st Hour Event > 패션/뷰티 > 뷰티 > 선케어/메이크업 > 아이메이크업'
6	'글로우 플레이 블러쉬 쏘 내추럴 SSG.COM>신세계백화점MAC>신규 컬렉션>글로우 플레이 블러쉬;(#M)SSG.COM>신세계백화점MAC>MAKE-UP ssg > 뷰티 > 메이크업 > 아이메이크업' '[~48%+10%즉시]피카소 홀리데이 레디백 추첨 ! 단독세트/스파츌라&팔레트/아이미속눈썹/메이크업브러쉬 [피카소] 생기창조 108 블러셔_-_- 쇼킹딜 홈>뷰티>네일아트/미용기기>미용소품/피부관리;11st>뷰티>네일아트/미용기기>미용소품/피부관리;11st>뷰티소품>메이크업소품>메이크업소품;11st > 뷰티 > 뷰티소품 > 메이크업소품 11st Hour Event > 패션/뷰티 > 뷰티 > 네일아트/미용기기 > 미용소품/피부관리' '[클렌징오일50ml] 글로우온 블러셔(리필+케이스) 세트 M335 소프트 핑크 LotteOn > 뷰티 > 명품화장품 > 메이크업 LotteOn > 뷰티 > 색조메이크업 > 블러셔'
1	'소프트 터치 오토 립 라이너 3호 밀키 브라운 ssg > 뷰티 > 메이크업 > 립메이크업 > 립라이너 ssg > 뷰티 > 메이크업 > 립메이크업 > 립라이너' 'Maybelline New York Color Sensational Shaping Lip Liner Makeup Gone Griege 001 oz 104 GONE GRIEGE_One Size (#M)SSG.COM/메이크업/립메이크업/립스틱 ssg > 뷰티 > 메이크업 > 립메이크업 > 립스틱' '로라 메르시에 롱웨어 립 라이너 - 프렌치 푸시아1.49g/0.05oz (#M)홈>스트로베리넷>향수
3	'루즈 언리미티드 마뜨 M BR771 ssg > 뷰티 > 스킨케어 > 클렌징;ssg > 뷰티 > 메이크업 > 치크메이크업;LOREAL > Ssg > 메이블린 > Generic > 아이브로우;ssg > 뷰티 > 스킨케어 > 클렌징 > 클렌징오일;ssg > 뷰티 > 명품화장품 > 메이크업 ssg > 뷰티 > 메이크업 > 치크메이크업 > 블러셔' '[하프클럽/메이블린뉴욕]슈퍼 스테이 립 잉크 크레용 1.2g 50_오운유어엠파이어/상세설명참조 LotteOn > 뷰티 > 클렌징 > 립아이리무버 LotteOn > 뷰티 > 클렌징 > 립아이리무버' '[단독] 퓨어 컬러 리바이탈라이징 크리스탈 밤 세트 (+파우치) 썬 크리스탈 LotteOn > 백화점 TAP > 명품화장품 > 메인 배너 (PC) LotteOn > 뷰티 > 럭셔리 스킨케어 > 에센스/세럼'
7	'클라란스 워터프루프 펜슬 - 04 피그0.29g/0.01oz (#M)홈>스트로베리넷>향수
10	'시슬리 스띨로 루미에르 - 3 소프트 베이지2.5ml/0.08oz (#M)홈>스트로베리넷>향수
2	'다슈 맨즈 컬러 체인지 모이스처 립밤 3.9g (#M)화장품/미용>남성화장품>메이크업 Naverstore > 화장품/미용 > 남성화장품 > 메이크업' '듀왑 립플럼퍼 오리지날 베놈 3.5ml 트와일라잇 베놈 홈>바디케어>립케어>립케어(스틱);(#M)홈>바디케어>립케어>스틱/밤타입 OLIVEYOUNG > 바디케어 > 립케어 > 스틱/밤타입' '라네즈 립 트리트먼트 밤 (볼륨&보습) 라네즈 립 트리트먼트 밤 [볼륨&보습] 홈>바디케어>립케어>립케어(밤);(#M)홈>바디케어>립케어>스틱/밤타입 OLIVEYOUNG > 바디케어 > 립케어 > 스틱/밤타입'
5	'록시땅 홈 퍼퓸 디퓨저 키트 4가지 향기 중 택 1 수플레드리베르떼리바이탈라이징디퓨저리필 (#M)뷰티>명품화장품>향수/홈프래그런스>캔들 CJmall > 뷰티 > 명품화장품 > 향수/홈프래그런스 > 캔들' '[해외직구/홍콩직배송] 어반디케이 네이키드 체리 ssg > 뷰티 > 메이크업 > 베이스메이크업 > 메이크업베이스 ssg > 뷰티 > 메이크업 > 베이스메이크업 > 메이크업베이스' '밀크바오밥 헤어케어 3종세트 3호 1세트 쿠팡 홈>뷰티>헤어>헤어세트;Coupang > 뷰티 > 헤어 > 헤어세트;(#M)쿠팡 홈>뷰티>선물세트/키트>선물세트>헤어케어 Coupang > 뷰티 > 선물세트/키트 > 선물세트 > 헤어케어'

Evaluation

Metrics

Label	Accuracy
all	0.4815

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_bt6_test_flat_top_cate")
# Run inference
preds = model("3CE 페이스 블러쉬 ROSE BEIGE 홈>전체상품;(#M)홈>FACE>치크 Naverstore > 화장품/미용 > 색조메이크업 > 블러셔")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	10	24.2404	79

Label	Training Sample Count
0	50
1	50
2	50
3	50
4	50
5	50
6	50
7	50
8	50
9	50
10	49

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
0.0012	1	0.4756	-
0.0583	50	0.447	-
0.1166	100	0.4629	-
0.1748	150	0.4327	-
0.2331	200	0.4182	-
0.2914	250	0.3863	-
0.3497	300	0.3492	-
0.4079	350	0.3277	-
0.4662	400	0.2987	-
0.5245	450	0.2729	-
0.5828	500	0.2637	-
0.6410	550	0.2554	-
0.6993	600	0.252	-
0.7576	650	0.2419	-
0.8159	700	0.2382	-
0.8741	750	0.239	-
0.9324	800	0.2294	-
0.9907	850	0.2274	-
1.0490	900	0.2237	-
1.1072	950	0.2241	-
1.1655	1000	0.2196	-
1.2238	1050	0.2164	-
1.2821	1100	0.2119	-
1.3403	1150	0.2048	-
1.3986	1200	0.2007	-
1.4569	1250	0.1969	-
1.5152	1300	0.1898	-
1.5734	1350	0.1857	-
1.6317	1400	0.1753	-
1.6900	1450	0.1703	-
1.7483	1500	0.1552	-
1.8065	1550	0.1481	-
1.8648	1600	0.1341	-
1.9231	1650	0.1254	-
1.9814	1700	0.1077	-
2.0396	1750	0.0895	-
2.0979	1800	0.0806	-
2.1562	1850	0.0674	-
2.2145	1900	0.0618	-
2.2727	1950	0.056	-
2.3310	2000	0.0549	-
2.3893	2050	0.0492	-
2.4476	2100	0.0438	-
2.5058	2150	0.0394	-
2.5641	2200	0.0395	-
2.6224	2250	0.0358	-
2.6807	2300	0.0373	-
2.7389	2350	0.0303	-
2.7972	2400	0.0321	-
2.8555	2450	0.0267	-
2.9138	2500	0.029	-
2.9720	2550	0.0314	-
3.0303	2600	0.031	-
3.0886	2650	0.019	-
3.1469	2700	0.02	-
3.2051	2750	0.0223	-
3.2634	2800	0.0206	-
3.3217	2850	0.0173	-
3.3800	2900	0.016	-
3.4382	2950	0.0181	-
3.4965	3000	0.0102	-
3.5548	3050	0.0078	-
3.6131	3100	0.0107	-
3.6713	3150	0.0094	-
3.7296	3200	0.0089	-
3.7879	3250	0.0097	-
3.8462	3300	0.0094	-
3.9044	3350	0.0111	-
3.9627	3400	0.0102	-
4.0210	3450	0.0091	-
4.0793	3500	0.0082	-
4.1375	3550	0.0048	-
4.1958	3600	0.0022	-
4.2541	3650	0.0007	-
4.3124	3700	0.0007	-
4.3706	3750	0.0012	-
4.4289	3800	0.0009	-
4.4872	3850	0.0006	-
4.5455	3900	0.0002	-
4.6037	3950	0.0002	-
4.6620	4000	0.0002	-
4.7203	4050	0.0002	-
4.7786	4100	0.0002	-
4.8368	4150	0.0001	-
4.8951	4200	0.0001	-
4.9534	4250	0.0001	-
5.0117	4300	0.0001	-
5.0699	4350	0.0001	-
5.1282	4400	0.0001	-
5.1865	4450	0.0001	-
5.2448	4500	0.0001	-
5.3030	4550	0.001	-
5.3613	4600	0.0003	-
5.4196	4650	0.0005	-
5.4779	4700	0.0014	-
5.5361	4750	0.0005	-
5.5944	4800	0.0016	-
5.6527	4850	0.0007	-
5.7110	4900	0.0003	-
5.7692	4950	0.0001	-
5.8275	5000	0.0005	-
5.8858	5050	0.0004	-
5.9441	5100	0.0003	-
6.0023	5150	0.0011	-
6.0606	5200	0.0001	-
6.1189	5250	0.0001	-
6.1772	5300	0.0001	-
6.2354	5350	0.0001	-
6.2937	5400	0.0002	-
6.3520	5450	0.0004	-
6.4103	5500	0.0009	-
6.4685	5550	0.0002	-
6.5268	5600	0.0001	-
6.5851	5650	0.0	-
6.6434	5700	0.0	-
6.7016	5750	0.0	-
6.7599	5800	0.0	-
6.8182	5850	0.0001	-
6.8765	5900	0.0006	-
6.9347	5950	0.0008	-
6.9930	6000	0.0013	-
7.0513	6050	0.0015	-
7.1096	6100	0.0007	-
7.1678	6150	0.003	-
7.2261	6200	0.0031	-
7.2844	6250	0.0013	-
7.3427	6300	0.0019	-
7.4009	6350	0.0025	-
7.4592	6400	0.0009	-
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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}
}