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mini1013/
master_item_top_bt12

Name: master_item_top_bt12
Availability: InStock

setfit

safetensors

roberta

sentence-transformers

text-classification

generated_from_setfit_trainer

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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:

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: klue/roberta-base
Classification head: a LogisticRegression instance
Maximum Sequence Length: 512 tokens
Number of Classes: 7 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
6	'이희 마블 에센스 헤어 팩트 다크브라운 본품 LotteOn > 뷰티 > 헤어스타일링 > 염색약 LotteOn > 뷰티 > 헤어스타일링 > 염색약' '더마클라센 스타일앤 볼륨짱짱 흑채 스프레이 블랙 120ml x5 MinSellAmount (#M)바디/헤어>헤어스타일링>염색약 Gmarket > 뷰티 > 바디/헤어 > 헤어스타일링 > 염색약' '이희 마블 에센스 헤어 팩트 블랙 본품 (#M)홈>화장품/미용>헤어케어>탈모케어 Naverstore > 화장품/미용 > 헤어케어 > 탈모케어'
2	'웰라 크레아틴 플러스 쉐이프 N 펌 에멀전/건강/파마약 (#M)화장품/미용>헤어스타일링>파마약>웨이브 AD > traverse > Naverstore > 화장품/미용 > 헤어케어 > 파마약 > 웨이브' '아모스 루미네이터 익스트림/하드/노멀/소프트/택 MinSellAmount (#M)바디/헤어>헤어스타일링>탈색제 Gmarket > 뷰티 > 바디/헤어 > 헤어스타일링 > 탈색제' '아모스 실키블루밍 펌 1제2제 SET 파마약 MinSellAmount (#M)바디/헤어>헤어스타일링>파마약 Gmarket > 뷰티 > 바디/헤어 > 헤어스타일링 > 파마약'
5	'300ml펌프형 아르드포 헤어젤 (#M)SSG.COM/헤어/바디/헤어스타일링/헤어왁스/젤 ssg > 뷰티 > 헤어/바디 > 헤어스타일링 > 헤어왁스/젤' '아르드포 헤어케어 헤어젤 180ml (#M)SSG.COM/헤어/바디/헤어기기/소품/기타헤어기기 ssg > 뷰티 > 헤어/바디 > 헤어기기/소품 > 기타헤어기기' '(NC)LG 아르드포 헤어젤 튜브 180ml (#M)SSG.COM/헤어/바디/헤어기기/소품/기타헤어기기 ssg > 뷰티 > 헤어/바디 > 헤어기기/소품 > 기타헤어기기'
0	'엘라스틴 살롱드컬러 새치염색약 100g x3개 +샴푸 증정 03)밝은갈색 ssg > 뷰티 > 헤어/바디 > 헤어스타일링 > 염색약;ssg > 뷰티 > 미용기기/소품 > 바디관리기기;ssg > 뷰티 > 헤어/바디 > 헤어케어 > 샴푸;ssg > 뷰티 > 헤어/바디 > 헤어케어;ssg > 뷰티 > 헤어/바디 > 헤어스타일링 ssg > 뷰티 > 헤어/바디 > 헤어스타일링' '댕기머리 포르테 프레스티지 4종옵션 /한방칼라크림 새치머리 염색약 4호 (자연갈색) (#M)11st>헤어케어>염색약>새치용염색약 11st > 뷰티 > 헤어케어 > 염색약 > 새치용염색약' '리엔 흑모비책 골드 염색약 1입 x3개 자연갈색 (#M)바디/헤어>헤어스타일링>염색약 Gmarket > 뷰티 > 바디/헤어 > 헤어스타일링 > 염색약'
4	'아르드포 헤어스프레이 280ml (#M)SSG.COM/헤어/바디/헤어기기/소품/기타헤어기기 ssg > 뷰티 > 헤어/바디 > 헤어기기/소품 > 기타헤어기기' '꽃을든남자 헤어케어시스템 헤어 스프레이(달콤한과일향) 300ml x 5개 MinSellAmount (#M)바디/헤어>헤어스타일링>헤어스프레이 Gmarket > 뷰티 > 바디/헤어 > 헤어스타일링 > 헤어스프레이' '헤드스파7 블루밍매직 헤어스타일러 50ml MinSellAmount (#M)바디/헤어>헤어케어>헤어트리트먼트 Gmarket > 뷰티 > 바디/헤어 > 헤어케어 > 헤어트리트먼트'
1	'미쟝센 컬링에센스2X 숏스타일 150ml x2 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 트리트먼트/헤어팩 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 트리트먼트/헤어팩' '미쟝센 컬링에센스2X 숏스타일 230ml 미쟝센 컬링에센스2X 숏스타일 230ml 홈>헤어케어>스타일링/에센스X>헤어에센스X;홈>헤어케어>스타일링/에센스>헤어에센스;(#M)홈>헤어케어>에센스>에센스 OLIVEYOUNG > 헤어케어 > 에센스 > 에센스' '4개)미쟝센스테이지컬렉션 컬링에센스2X 탄력웨이브150ml 선택없음 Coupang > 뷰티 > 헤어 > 헤어스타일링 > 컬크림;(#M)쿠팡 홈>뷰티>헤어>헤어스타일링>컬크림 Coupang > 뷰티 > 헤어 > 헤어스타일링 > 컬크림'
3	'128 브러쉬 단품없음 LotteOn > 뷰티 > 뷰티기기 > 액세서리/소모품 LotteOn > 뷰티 > 뷰티기기 > 액세서리/소모품' '리엔 (엘라스틴) 살롱드 컬러 팡팡 헤어쿠션 (짙은갈색) x 3개 짙은갈색 (#M)바디/헤어>헤어스타일링>염색약 Gmarket > 뷰티 > 바디/헤어 > 헤어스타일링 > 염색약' '[이지피지] 해피펀치 헤어 커버스틱 3.5g (옵션) 옵션:1호 라이트 헤어 쿠팡 홈>뷰티>뷰티소품>피부관리기>롤러/마사지기;쿠팡 홈>선물스토어>생일선물>여성선물>이미용가전>롤링미용기기;쿠팡 홈>선물스토어>생일>이미용가전>셀프스킨케어>롤링미용기기;(#M)쿠팡 홈>뷰티>헤어>염색/파마>헤어메이크업>헤어섀도/마스카라 Coupang > 뷰티 > 헤어 > 염색/파마 > 헤어메이크업 > 헤어섀도/마스카라'

Evaluation

Metrics

Label	Accuracy
all	0.9465

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

python

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("mini1013/master_item_top_bt12")
# Run inference
preds = model("아모스 스타일 익스프레션 몰딩 글레이즈300ml MinSellAmount (#M)바디/헤어>헤어스타일링>헤어글레이즈 Gmarket > 뷰티 > 바디/헤어 > 헤어스타일링 > 헤어글레이즈")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	11	22.4371	93

Label	Training Sample Count
0	50
1	50
2	50
3	50
4	50
5	50
6	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
0.0018	1	0.5286	-
0.0914	50	0.4469	-
0.1828	100	0.4235	-
0.2742	150	0.361	-
0.3656	200	0.2736	-
0.4570	250	0.1705	-
0.5484	300	0.0988	-
0.6399	350	0.0709	-
0.7313	400	0.0516	-
0.8227	450	0.0467	-
0.9141	500	0.0477	-
1.0055	550	0.0442	-
1.0969	600	0.0241	-
1.1883	650	0.0238	-
1.2797	700	0.0213	-
1.3711	750	0.0248	-
1.4625	800	0.0202	-
1.5539	850	0.0209	-
1.6453	900	0.0206	-
1.7367	950	0.0203	-
1.8282	1000	0.0229	-
1.9196	1050	0.011	-
2.0110	1100	0.0003	-
2.1024	1150	0.0002	-
2.1938	1200	0.0002	-
2.2852	1250	0.0001	-
2.3766	1300	0.0003	-
2.4680	1350	0.0001	-
2.5594	1400	0.0001	-
2.6508	1450	0.0	-
2.7422	1500	0.0	-
2.8336	1550	0.0	-
2.9250	1600	0.0	-
3.0165	1650	0.0	-
3.1079	1700	0.0	-
3.1993	1750	0.0	-
3.2907	1800	0.0	-
3.3821	1850	0.0	-
3.4735	1900	0.0	-
3.5649	1950	0.0004	-
3.6563	2000	0.0003	-
3.7477	2050	0.0004	-
3.8391	2100	0.001	-
3.9305	2150	0.0005	-
4.0219	2200	0.0	-
4.1133	2250	0.0	-
4.2048	2300	0.0	-
4.2962	2350	0.0	-
4.3876	2400	0.0	-
4.4790	2450	0.0	-
4.5704	2500	0.0	-
4.6618	2550	0.0	-
4.7532	2600	0.0	-
4.8446	2650	0.0	-
4.9360	2700	0.0003	-
5.0274	2750	0.0	-
5.1188	2800	0.0	-
5.2102	2850	0.0	-
5.3016	2900	0.0	-
5.3931	2950	0.0	-
5.4845	3000	0.0	-
5.5759	3050	0.0	-
5.6673	3100	0.0	-
5.7587	3150	0.0	-
5.8501	3200	0.0	-
5.9415	3250	0.0	-
6.0329	3300	0.0	-
6.1243	3350	0.0001	-
6.2157	3400	0.0009	-
6.3071	3450	0.0008	-
6.3985	3500	0.0007	-
6.4899	3550	0.0001	-
6.5814	3600	0.0	-
6.6728	3650	0.0	-
6.7642	3700	0.0	-
6.8556	3750	0.0	-
6.9470	3800	0.0	-
7.0384	3850	0.0	-
7.1298	3900	0.0	-
7.2212	3950	0.0	-
7.3126	4000	0.0	-
7.4040	4050	0.0	-
7.4954	4100	0.0	-
7.5868	4150	0.0	-
7.6782	4200	0.0	-
7.7697	4250	0.0002	-
7.8611	4300	0.0	-
7.9525	4350	0.0	-
8.0439	4400	0.0	-
8.1353	4450	0.0	-
8.2267	4500	0.0	-
8.3181	4550	0.0	-
8.4095	4600	0.0	-
8.5009	4650	0.0	-
8.5923	4700	0.0	-
8.6837	4750	0.0	-
8.7751	4800	0.0	-
8.8665	4850	0.0	-
8.9580	4900	0.0	-
9.0494	4950	0.0	-
9.1408	5000	0.0	-
9.2322	5050	0.0	-
9.3236	5100	0.0	-
9.4150	5150	0.0	-
9.5064	5200	0.0003	-
9.5978	5250	0.0013	-
9.6892	5300	0.0	-
9.7806	5350	0.0	-
9.8720	5400	0.0	-
9.9634	5450	0.0	-
10.0548	5500	0.0	-
10.1463	5550	0.0	-
10.2377	5600	0.0	-
10.3291	5650	0.0	-
10.4205	5700	0.0	-
10.5119	5750	0.0	-
10.6033	5800	0.0	-
10.6947	5850	0.0	-
10.7861	5900	0.0	-
10.8775	5950	0.0	-
10.9689	6000	0.0	-
11.0603	6050	0.0	-
11.1517	6100	0.0	-
11.2431	6150	0.0	-
11.3346	6200	0.0	-
11.4260	6250	0.0	-
11.5174	6300	0.0	-
11.6088	6350	0.0	-
11.7002	6400	0.0	-
11.7916	6450	0.0	-
11.8830	6500	0.0	-
11.9744	6550	0.0	-
12.0658	6600	0.0	-
12.1572	6650	0.0	-
12.2486	6700	0.0	-
12.3400	6750	0.0	-
12.4314	6800	0.0	-
12.5229	6850	0.0	-
12.6143	6900	0.0	-
12.7057	6950	0.0	-
12.7971	7000	0.0	-
12.8885	7050	0.0	-
12.9799	7100	0.0	-
13.0713	7150	0.0	-
13.1627	7200	0.0	-
13.2541	7250	0.0	-
13.3455	7300	0.0	-
13.4369	7350	0.0	-
13.5283	7400	0.0	-
13.6197	7450	0.0	-
13.7112	7500	0.0	-
13.8026	7550	0.0	-
13.8940	7600	0.0	-
13.9854	7650	0.0	-
14.0768	7700	0.0	-
14.1682	7750	0.0	-
14.2596	7800	0.0	-
14.3510	7850	0.0	-
14.4424	7900	0.0	-
14.5338	7950	0.0	-
14.6252	8000	0.0	-
14.7166	8050	0.0	-
14.8080	8100	0.0	-
14.8995	8150	0.0	-
14.9909	8200	0.0	-
15.0823	8250	0.0	-
15.1737	8300	0.0	-
15.2651	8350	0.0	-
15.3565	8400	0.0	-
15.4479	8450	0.0	-
15.5393	8500	0.0	-
15.6307	8550	0.0	-
15.7221	8600	0.0	-
15.8135	8650	0.0	-
15.9049	8700	0.0	-
15.9963	8750	0.0	-
16.0878	8800	0.0	-
16.1792	8850	0.0	-
16.2706	8900	0.0	-
16.3620	8950	0.0	-
16.4534	9000	0.0	-
16.5448	9050	0.0	-
16.6362	9100	0.0	-
16.7276	9150	0.0	-
16.8190	9200	0.0	-
16.9104	9250	0.0	-
17.0018	9300	0.0	-
17.0932	9350	0.0	-
17.1846	9400	0.0	-
17.2761	9450	0.0	-
17.3675	9500	0.0	-
17.4589	9550	0.0	-
17.5503	9600	0.0	-
17.6417	9650	0.0	-
17.7331	9700	0.0	-
17.8245	9750	0.0	-
17.9159	9800	0.0	-
18.0073	9850	0.0	-
18.0987	9900	0.0	-
18.1901	9950	0.0	-
18.2815	10000	0.0	-
18.3729	10050	0.0	-
18.4644	10100	0.0	-
18.5558	10150	0.0	-
18.6472	10200	0.0	-
18.7386	10250	0.0	-
18.8300	10300	0.0	-
18.9214	10350	0.0	-
19.0128	10400	0.0	-
19.1042	10450	0.0	-
19.1956	10500	0.0	-
19.2870	10550	0.0	-
19.3784	10600	0.0	-
19.4698	10650	0.0	-
19.5612	10700	0.0	-
19.6527	10750	0.0	-
19.7441	10800	0.0	-
19.8355	10850	0.0	-
19.9269	10900	0.0	-
20.0183	10950	0.0	-
20.1097	11000	0.0	-
20.2011	11050	0.0	-
20.2925	11100	0.0	-
20.3839	11150	0.0	-
20.4753	11200	0.0	-
20.5667	11250	0.0	-
20.6581	11300	0.0	-
20.7495	11350	0.0	-
20.8410	11400	0.0	-
20.9324	11450	0.0	-
21.0238	11500	0.0	-
21.1152	11550	0.0	-
21.2066	11600	0.0	-
21.2980	11650	0.0	-
21.3894	11700	0.0	-
21.4808	11750	0.0	-
21.5722	11800	0.0	-
21.6636	11850	0.0	-
21.7550	11900	0.0	-
21.8464	11950	0.0	-
21.9378	12000	0.0	-
22.0293	12050	0.0	-
22.1207	12100	0.0	-
22.2121	12150	0.0	-
22.3035	12200	0.0	-
22.3949	12250	0.0	-
22.4863	12300	0.0	-
22.5777	12350	0.0	-
22.6691	12400	0.0	-
22.7605	12450	0.0	-
22.8519	12500	0.0	-
22.9433	12550	0.0	-
23.0347	12600	0.0	-
23.1261	12650	0.0	-
23.2176	12700	0.0	-
23.3090	12750	0.0	-
23.4004	12800	0.0	-
23.4918	12850	0.0	-
23.5832	12900	0.0	-
23.6746	12950	0.0	-
23.7660	13000	0.0	-
23.8574	13050	0.0	-
23.9488	13100	0.0	-
24.0402	13150	0.0	-
24.1316	13200	0.0	-
24.2230	13250	0.0	-
24.3144	13300	0.0	-
24.4059	13350	0.0	-
24.4973	13400	0.0	-
24.5887	13450	0.0	-
24.6801	13500	0.0	-
24.7715	13550	0.0	-
24.8629	13600	0.0	-
24.9543	13650	0.0	-
25.0457	13700	0.0	-
25.1371	13750	0.0	-
25.2285	13800	0.0	-
25.3199	13850	0.0	-
25.4113	13900	0.0	-
25.5027	13950	0.0	-
25.5941	14000	0.0	-
25.6856	14050	0.0	-
25.7770	14100	0.0	-
25.8684	14150	0.0	-
25.9598	14200	0.0	-
26.0512	14250	0.0	-
26.1426	14300	0.0	-
26.2340	14350	0.0	-
26.3254	14400	0.0	-
26.4168	14450	0.0	-
26.5082	14500	0.0	-
26.5996	14550	0.0	-
26.6910	14600	0.0	-
26.7824	14650	0.0	-
26.8739	14700	0.0	-
26.9653	14750	0.0	-
27.0567	14800	0.0	-
27.1481	14850	0.0	-
27.2395	14900	0.0	-
27.3309	14950	0.0	-
27.4223	15000	0.0	-
27.5137	15050	0.0	-
27.6051	15100	0.0	-
27.6965	15150	0.0	-
27.7879	15200	0.0	-
27.8793	15250	0.0	-
27.9707	15300	0.0	-
28.0622	15350	0.0	-
28.1536	15400	0.0	-
28.2450	15450	0.0	-
28.3364	15500	0.0	-
28.4278	15550	0.0	-
28.5192	15600	0.0	-
28.6106	15650	0.0	-
28.7020	15700	0.0	-
28.7934	15750	0.0	-
28.8848	15800	0.0	-
28.9762	15850	0.0	-
29.0676	15900	0.0	-
29.1590	15950	0.0	-
29.2505	16000	0.0	-
29.3419	16050	0.0	-
29.4333	16100	0.0	-
29.5247	16150	0.0	-
29.6161	16200	0.0	-
29.7075	16250	0.0	-
29.7989	16300	0.0	-
29.8903	16350	0.0	-
29.9817	16400	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

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}
}