mini1013/master_cate_bt_top8_test

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: 5 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
2	'이니스프리 노세범 선쿠션 SPF50+ PA++++ 14g × 2개 (#M)위메프 > 뷰티 > 메이크업 > 베이스 메이크업 > 파우더/팩트 위메프 > 뷰티 > 메이크업 > 베이스 메이크업 > 파우더/팩트' '스킨 세팅 톤업 선 쿠션(리필포함) + 추가구성품 톤업 선 쿠션 LotteOn > 백화점 > 뷰티 > 상단 배너 (Mobile) LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 쿠션/팩트' '이니스프리 노세범 선쿠션 리필 14g 1 +1 (#M)쿠팡 홈>뷰티>스킨케어>선케어/태닝>선케어>선스틱 Coupang > 뷰티 > 로드샵 > 스킨케어 > 선케어/태닝'
1	'SUNDANCE 썬댄스 햇빛 차단+태닝 선스프레이 LSF 50, 200ml ssg > 뷰티 > 스킨케어 > 선케어 > 선스프레이 ssg > 뷰티 > 스킨케어 > 선케어 > 선스프레이' '리더스 여름자외선 썬버디 올 오버 선 스프레이 180ml MinSellAmount (#M)화장품/향수>선케어>선스프레이 Gmarket > 뷰티 > 화장품/향수 > 선케어 > 선스프레이' '온더바디 헬로키티 에코 썬 스프레이 120ml+120ml 기획세트 (#M)홈>화장품/미용>선케어>선케어세트 Naverstore > 화장품/미용 > 선케어 > 선케어세트'
0	'[피지오겔] [정가 85,000원] 레드 수딩 AI 에어리 썬스틱 1+1 특별기획 롯데홈쇼핑 > 뷰티 > 남성화장품 LotteOn > 뷰티 > 남성화장품 > 선크림' '[빌리프][2106] 해피 보 이지워시 선스틱 18g 세트(타임스퀘어점패션관) (#M)11st>선케어>선밤>선밤 11st > 뷰티 > 선케어 > 선밤 > 선밤' '피지오겔 레드 수딩 AI 에어리 썬스틱 7g 1+1(2개) (#M)홈>스킨케어>선케어 HMALL > 뷰티 > 스킨케어 > 선케어'
4	'오스트레일리안골드 헴프네이션 오리지널 탠 익스텐더 바디로션 535ml (#M)SSG.COM/스킨케어/선케어/태닝 ssg > 뷰티 > 스킨케어 > 선케어 > 태닝' '수딩앤모이스처 알로에베라92%수딩젤300ml (#M)홈>화장품/미용>바디케어>바디로션 Naverstore > 화장품/미용 > 바디케어 > 바디로션' '세인트 트로페즈 셀프 탠 익스프레스 어드밴스드 브론징 무스 200ml (#M)SSG.COM/스킨케어/선케어/태닝 ssg > 뷰티 > 스킨케어 > 선케어 > 태닝'
3	'[맥퀸뉴욕] 1+ 1 UV 데일리 모이스처(수분) 선크림 1+1 UV 데일리 모이스처 선크림 (#M)SSG.COM/메이크업/립메이크업/립글로스 ssg > 뷰티 > 메이크업 > 아이메이크업 > 아이라이너' '[공식] 더마비 10주년 바디로션/기획세트/멀티오일/프레쉬/크림/워시 1+1 S11.(애브리데이) 대용량 선블록 200ml×2개_S1.튜브견본(랜덤) 쇼킹딜 홈;쇼킹딜 홈>뷰티>바디/향수>바디케어;11st>뷰티>바디/향수>바디케어;11st>바디케어>바디로션>바디로션;11st > 뷰티 > 바디케어 > 바디로션 11st Hour Event > 패션/뷰티 > 뷰티 > 바디/향수 > 바디케어' '[20%찜+T11%+묶음+당일 ] 롬앤 11번가 런칭! 모든 취향 취급 중! 밀크 그로서리 외 BEST 1+1 옵션31. 제로 선 클린 단품_01 프레쉬 쇼킹딜 홈>뷰티>선케어/메이크업>립/치크메이크업;11st>메이크업>립메이크업>립틴트;11st>뷰티>선케어/메이크업>립/치크메이크업;11st>뷰티>선케어/메이크업>아이메이크업;11st>메이크업>아이메이크업>마스카라;11st Hour Event > 패션/뷰티 > 뷰티 > 선케어/메이크업 > 립/치크메이크업 11st Hour Event > 패션/뷰티 > 뷰티 > 선케어/메이크업 > 아이메이크업'

Evaluation

Metrics

Label	Accuracy
all	0.4903

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_top8_test")
# Run inference
preds = model("엔프라니 옴므 선블록 썬크림 남성용 선크림  (#M)화장품/미용>남성화장품>선크림 Naverstore > 화장품/미용 > 남성화장품 > 선크림")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	11	21.656	72

Label	Training Sample Count
0	50
1	50
2	50
3	50
4	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
0.0026	1	0.4513	-
0.1279	50	0.4435	-
0.2558	100	0.4063	-
0.3836	150	0.3413	-
0.5115	200	0.2997	-
0.6394	250	0.2434	-
0.7673	300	0.1724	-
0.8951	350	0.1334	-
1.0230	400	0.1078	-
1.1509	450	0.0997	-
1.2788	500	0.0937	-
1.4066	550	0.0933	-
1.5345	600	0.0909	-
1.6624	650	0.0897	-
1.7903	700	0.0842	-
1.9182	750	0.0741	-
2.0460	800	0.0764	-
2.1739	850	0.0745	-
2.3018	900	0.0733	-
2.4297	950	0.0748	-
2.5575	1000	0.0718	-
2.6854	1050	0.0568	-
2.8133	1100	0.0415	-
2.9412	1150	0.0256	-
3.0691	1200	0.0233	-
3.1969	1250	0.0128	-
3.3248	1300	0.0088	-
3.4527	1350	0.0066	-
3.5806	1400	0.0058	-
3.7084	1450	0.006	-
3.8363	1500	0.0058	-
3.9642	1550	0.0039	-
4.0921	1600	0.0043	-
4.2199	1650	0.0033	-
4.3478	1700	0.0059	-
4.4757	1750	0.0065	-
4.6036	1800	0.0061	-
4.7315	1850	0.0052	-
4.8593	1900	0.0054	-
4.9872	1950	0.0043	-
5.1151	2000	0.0064	-
5.2430	2050	0.0042	-
5.3708	2100	0.0046	-
5.4987	2150	0.0038	-
5.6266	2200	0.0031	-
5.7545	2250	0.0021	-
5.8824	2300	0.0006	-
6.0102	2350	0.0003	-
6.1381	2400	0.0001	-
6.2660	2450	0.0002	-
6.3939	2500	0.0	-
6.5217	2550	0.0	-
6.6496	2600	0.0001	-
6.7775	2650	0.0	-
6.9054	2700	0.0	-
7.0332	2750	0.0	-
7.1611	2800	0.0	-
7.2890	2850	0.0	-
7.4169	2900	0.0	-
7.5448	2950	0.0	-
7.6726	3000	0.0	-
7.8005	3050	0.0	-
7.9284	3100	0.0	-
8.0563	3150	0.0	-
8.1841	3200	0.0	-
8.3120	3250	0.0	-
8.4399	3300	0.0	-
8.5678	3350	0.0	-
8.6957	3400	0.0	-
8.8235	3450	0.0	-
8.9514	3500	0.0	-
9.0793	3550	0.0	-
9.2072	3600	0.0	-
9.3350	3650	0.0	-
9.4629	3700	0.0	-
9.5908	3750	0.0	-
9.7187	3800	0.0	-
9.8465	3850	0.0	-
9.9744	3900	0.0	-
10.1023	3950	0.0	-
10.2302	4000	0.0	-
10.3581	4050	0.0	-
10.4859	4100	0.0	-
10.6138	4150	0.0	-
10.7417	4200	0.0	-
10.8696	4250	0.0	-
10.9974	4300	0.0	-
11.1253	4350	0.0	-
11.2532	4400	0.0	-
11.3811	4450	0.0	-
11.5090	4500	0.0	-
11.6368	4550	0.0	-
11.7647	4600	0.0	-
11.8926	4650	0.0	-
12.0205	4700	0.0	-
12.1483	4750	0.0	-
12.2762	4800	0.0	-
12.4041	4850	0.0	-
12.5320	4900	0.0	-
12.6598	4950	0.0	-
12.7877	5000	0.0	-
12.9156	5050	0.0	-
13.0435	5100	0.0	-
13.1714	5150	0.0	-
13.2992	5200	0.0	-
13.4271	5250	0.0	-
13.5550	5300	0.0	-
13.6829	5350	0.0	-
13.8107	5400	0.0	-
13.9386	5450	0.0	-
14.0665	5500	0.0	-
14.1944	5550	0.0	-
14.3223	5600	0.0	-
14.4501	5650	0.0	-
14.5780	5700	0.0	-
14.7059	5750	0.0	-
14.8338	5800	0.0	-
14.9616	5850	0.0	-
15.0895	5900	0.0	-
15.2174	5950	0.0	-
15.3453	6000	0.0	-
15.4731	6050	0.0	-
15.6010	6100	0.0	-
15.7289	6150	0.0	-
15.8568	6200	0.0	-
15.9847	6250	0.0	-
16.1125	6300	0.0	-
16.2404	6350	0.0	-
16.3683	6400	0.0	-
16.4962	6450	0.0	-
16.6240	6500	0.0	-
16.7519	6550	0.0	-
16.8798	6600	0.0	-
17.0077	6650	0.0	-
17.1355	6700	0.0	-
17.2634	6750	0.0	-
17.3913	6800	0.0	-
17.5192	6850	0.0	-
17.6471	6900	0.0	-
17.7749	6950	0.0	-
17.9028	7000	0.0	-
18.0307	7050	0.0	-
18.1586	7100	0.0	-
18.2864	7150	0.0	-
18.4143	7200	0.0	-
18.5422	7250	0.0	-
18.6701	7300	0.0	-
18.7980	7350	0.0	-
18.9258	7400	0.0	-
19.0537	7450	0.0	-
19.1816	7500	0.0	-
19.3095	7550	0.0	-
19.4373	7600	0.0	-
19.5652	7650	0.0	-
19.6931	7700	0.0	-
19.8210	7750	0.0	-
19.9488	7800	0.0	-
20.0767	7850	0.0	-
20.2046	7900	0.0	-
20.3325	7950	0.0	-
20.4604	8000	0.0	-
20.5882	8050	0.0	-
20.7161	8100	0.0	-
20.8440	8150	0.0	-
20.9719	8200	0.0	-
21.0997	8250	0.0	-
21.2276	8300	0.0	-
21.3555	8350	0.0	-
21.4834	8400	0.0	-
21.6113	8450	0.0	-
21.7391	8500	0.0	-
21.8670	8550	0.0	-
21.9949	8600	0.0	-
22.1228	8650	0.0	-
22.2506	8700	0.0	-
22.3785	8750	0.0	-
22.5064	8800	0.0	-
22.6343	8850	0.0	-
22.7621	8900	0.0	-
22.8900	8950	0.0	-
23.0179	9000	0.0	-
23.1458	9050	0.0	-
23.2737	9100	0.0	-
23.4015	9150	0.0	-
23.5294	9200	0.0	-
23.6573	9250	0.0	-
23.7852	9300	0.0	-
23.9130	9350	0.0	-
24.0409	9400	0.0	-
24.1688	9450	0.0	-
24.2967	9500	0.0	-
24.4246	9550	0.0	-
24.5524	9600	0.0	-
24.6803	9650	0.0	-
24.8082	9700	0.0	-
24.9361	9750	0.0	-
25.0639	9800	0.0	-
25.1918	9850	0.0	-
25.3197	9900	0.0	-
25.4476	9950	0.0	-
25.5754	10000	0.0	-
25.7033	10050	0.0	-
25.8312	10100	0.0	-
25.9591	10150	0.0	-
26.0870	10200	0.0	-
26.2148	10250	0.0	-
26.3427	10300	0.0	-
26.4706	10350	0.0	-
26.5985	10400	0.0	-
26.7263	10450	0.0	-
26.8542	10500	0.0	-
26.9821	10550	0.0	-
27.1100	10600	0.0	-
27.2379	10650	0.0	-
27.3657	10700	0.0	-
27.4936	10750	0.0	-
27.6215	10800	0.0	-
27.7494	10850	0.0	-
27.8772	10900	0.0	-
28.0051	10950	0.0	-
28.1330	11000	0.0	-
28.2609	11050	0.0	-
28.3887	11100	0.0	-
28.5166	11150	0.0	-
28.6445	11200	0.0	-
28.7724	11250	0.0	-
28.9003	11300	0.0	-
29.0281	11350	0.0	-
29.1560	11400	0.0	-
29.2839	11450	0.0	-
29.4118	11500	0.0	-
29.5396	11550	0.0	-
29.6675	11600	0.0	-
29.7954	11650	0.0	-
29.9233	11700	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}
}