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mini1013/
master_cate_top_bt6_3

Name: master_cate_top_bt6_3
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: 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
5	'[에뛰드] 마이뷰티툴 속눈썹 1ea 5호 홈>화장소품;홈>TOOL;(#M)홈>배송비 절약템 🛒 Naverstore > 화장품/미용 > 뷰티소품 > 아이소품 > 속눈썹/속눈썹펌제' '더툴랩 더 스타일 래쉬 볼륨 TSL003 블랙 × 45개 (#M)쿠팡 홈>뷰티>뷰티소품>아이소품>속눈썹관리 소품 Coupang > 뷰티 > 뷰티소품 > 아이소품 > 속눈썹관리 소품' '더툴랩 해피림 아이래쉬 내추럴 가닥속눈썹 1pack 11.5N (#M)화장품/미용>뷰티소품>아이소품>속눈썹/속눈썹펌제 Naverstore > 화장품/미용 > 뷰티소품 > 아이소품 > 속눈썹/속눈썹펌제'
1	'트위저맨 Tweezerman 스테인리스 브로우 쉐이핑 가위 및 브러시 521626 (#M)홈>화장품/미용>뷰티소품>헤어소품>미용가위 Naverstore > 화장품/미용 > 뷰티소품 > 헤어소품 > 미용가위' '트위저맨 스테인리스 브로우 셰이핑 시져 브러쉬 70238 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 베이스/프라이머 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 베이스/프라이머' '트위저맨 스테인리스 브로우 셰이핑 시져 브러쉬 70238 ssg > 뷰티 > 메이크업 > 베이스메이크업 > 파운데이션 ssg > 뷰티 > 메이크업 > 베이스메이크업 > 파운데이션'
0	'피카소 속눈썹 빗 피카소 속눈썹 빗 (#M)홈>미용소품>기타소품>기타미용소품 OLIVEYOUNG > 미용소품 > 기타소품 > 기타미용소품' '트위저맨 프로페셔널 폴딩 아이래쉬콤브 1개 (#M)쿠팡 홈>뷰티>뷰티소품>페이스소품>브러쉬 Coupang > 뷰티 > 뷰티소품 > 페이스소품 > 브러쉬' '속눈썹롯드 속눈썹펌롯드 L컬(SHARP) (#M)홈>화장품/미용>뷰티소품>아이소품>기타아이소품 Naverstore > 화장품/미용 > 뷰티소품 > 아이소품 > 기타아이소품'
3	'[Tweezerman] 트위저맨 클래식 속눈썹 뷰러 로즈 골드 1개입 (#M)홈>화장품/미용>뷰티소품>메이크업브러시>브러시세트 Naverstore > 화장품/미용 > 뷰티소품 > 메이크업브러시 > 브러시세트' '[에뛰드] 마이 뷰티툴 뷰러 (1EA) (#M)홈>TOOL Naverstore > 화장품/미용 > 뷰티소품 > 아이소품 > 뷰러' '시세이도 213뷰러 전체뷰러 속눈썹 고데기+뷰러리필 LotteOn > 뷰티 > 뷰티소품 > 아이소품 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품 > 속눈썹관리'
4	'e.l.f. 듀얼 펜슬 샤프너 10세트 (#M)쿠팡 홈>뷰티>뷰티소품>아이소품>족집게/샤프너 Coupang > 뷰티 > 뷰티소품 > 아이소품 > 족집게/샤프너' 'e.l.f. 듀얼 펜슬 샤프너 혼합 색상 × 6개입 (#M)쿠팡 홈>뷰티>뷰티소품>아이소품>족집게/샤프너 Coupang > 뷰티 > 뷰티소품 > 아이소품 > 족집게/샤프너' '[맥]펜슬 샤프너 (#M)홈>화장품/미용>뷰티소품>아이소품>샤프너 Naverstore > 화장품/미용 > 뷰티소품 > 아이소품 > 샤프너'

Evaluation

Metrics

Label	Accuracy
all	0.9622

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_cate_top_bt6_3")
# Run inference
preds = model("Tweezerman 클래식 아이래쉬 컬러 속눈썹 뷰러  (#M)화장품/미용>뷰티소품>아이소품>눈썹칼 Naverstore > 화장품/미용 > 뷰티소품 > 아이소품 > 눈썹칼")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	13	20.7017	46

Label	Training Sample Count
0	50
1	9
3	50
4	22
5	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
0.0035	1	0.4788	-
0.1767	50	0.4758	-
0.3534	100	0.4629	-
0.5300	150	0.3827	-
0.7067	200	0.2023	-
0.8834	250	0.0454	-
1.0601	300	0.0031	-
1.2367	350	0.0001	-
1.4134	400	0.0001	-
1.5901	450	0.0	-
1.7668	500	0.0	-
1.9435	550	0.0001	-
2.1201	600	0.0	-
2.2968	650	0.0	-
2.4735	700	0.0	-
2.6502	750	0.0	-
2.8269	800	0.0	-
3.0035	850	0.0	-
3.1802	900	0.0	-
3.3569	950	0.0	-
3.5336	1000	0.0	-
3.7102	1050	0.0	-
3.8869	1100	0.0	-
4.0636	1150	0.0	-
4.2403	1200	0.0	-
4.4170	1250	0.0001	-
4.5936	1300	0.0012	-
4.7703	1350	0.0006	-
4.9470	1400	0.0	-
5.1237	1450	0.0	-
5.3004	1500	0.0	-
5.4770	1550	0.0	-
5.6537	1600	0.0	-
5.8304	1650	0.0	-
6.0071	1700	0.0	-
6.1837	1750	0.0	-
6.3604	1800	0.0	-
6.5371	1850	0.0	-
6.7138	1900	0.0	-
6.8905	1950	0.0	-
7.0671	2000	0.0	-
7.2438	2050	0.0	-
7.4205	2100	0.0	-
7.5972	2150	0.0	-
7.7739	2200	0.0	-
7.9505	2250	0.0	-
8.1272	2300	0.0	-
8.3039	2350	0.0	-
8.4806	2400	0.0	-
8.6572	2450	0.0	-
8.8339	2500	0.0	-
9.0106	2550	0.0	-
9.1873	2600	0.0	-
9.3640	2650	0.0	-
9.5406	2700	0.0	-
9.7173	2750	0.0	-
9.8940	2800	0.0	-
10.0707	2850	0.0	-
10.2473	2900	0.0	-
10.4240	2950	0.0	-
10.6007	3000	0.0	-
10.7774	3050	0.0	-
10.9541	3100	0.0	-
11.1307	3150	0.0	-
11.3074	3200	0.0	-
11.4841	3250	0.0	-
11.6608	3300	0.0	-
11.8375	3350	0.0	-
12.0141	3400	0.0	-
12.1908	3450	0.0	-
12.3675	3500	0.0	-
12.5442	3550	0.0	-
12.7208	3600	0.0	-
12.8975	3650	0.0	-
13.0742	3700	0.0	-
13.2509	3750	0.0	-
13.4276	3800	0.0	-
13.6042	3850	0.0	-
13.7809	3900	0.0	-
13.9576	3950	0.0	-
14.1343	4000	0.0	-
14.3110	4050	0.0	-
14.4876	4100	0.0	-
14.6643	4150	0.0	-
14.8410	4200	0.0	-
15.0177	4250	0.0	-
15.1943	4300	0.0	-
15.3710	4350	0.0	-
15.5477	4400	0.0	-
15.7244	4450	0.0	-
15.9011	4500	0.0	-
16.0777	4550	0.0	-
16.2544	4600	0.0	-
16.4311	4650	0.0	-
16.6078	4700	0.0	-
16.7845	4750	0.0	-
16.9611	4800	0.0	-
17.1378	4850	0.0	-
17.3145	4900	0.0	-
17.4912	4950	0.0	-
17.6678	5000	0.0	-
17.8445	5050	0.0	-
18.0212	5100	0.0	-
18.1979	5150	0.0	-
18.3746	5200	0.0	-
18.5512	5250	0.0	-
18.7279	5300	0.0	-
18.9046	5350	0.0	-
19.0813	5400	0.0	-
19.2580	5450	0.0	-
19.4346	5500	0.0	-
19.6113	5550	0.0	-
19.7880	5600	0.0	-
19.9647	5650	0.0	-
20.1413	5700	0.0	-
20.3180	5750	0.0	-
20.4947	5800	0.0	-
20.6714	5850	0.0	-
20.8481	5900	0.0	-
21.0247	5950	0.0	-
21.2014	6000	0.0	-
21.3781	6050	0.0	-
21.5548	6100	0.0	-
21.7314	6150	0.0	-
21.9081	6200	0.0	-
22.0848	6250	0.0	-
22.2615	6300	0.0	-
22.4382	6350	0.0	-
22.6148	6400	0.0	-
22.7915	6450	0.0	-
22.9682	6500	0.0	-
23.1449	6550	0.0	-
23.3216	6600	0.0	-
23.4982	6650	0.0	-
23.6749	6700	0.0	-
23.8516	6750	0.0	-
24.0283	6800	0.0	-
24.2049	6850	0.0	-
24.3816	6900	0.0	-
24.5583	6950	0.0	-
24.7350	7000	0.0	-
24.9117	7050	0.0	-
25.0883	7100	0.0	-
25.2650	7150	0.0	-
25.4417	7200	0.0	-
25.6184	7250	0.0	-
25.7951	7300	0.0	-
25.9717	7350	0.0	-
26.1484	7400	0.0	-
26.3251	7450	0.0	-
26.5018	7500	0.0	-
26.6784	7550	0.0	-
26.8551	7600	0.0	-
27.0318	7650	0.0	-
27.2085	7700	0.0	-
27.3852	7750	0.0	-
27.5618	7800	0.0	-
27.7385	7850	0.0	-
27.9152	7900	0.0	-
28.0919	7950	0.0	-
28.2686	8000	0.0	-
28.4452	8050	0.0	-
28.6219	8100	0.0	-
28.7986	8150	0.0	-
28.9753	8200	0.0	-
29.1519	8250	0.0	-
29.3286	8300	0.0	-
29.5053	8350	0.0	-
29.6820	8400	0.0	-
29.8587	8450	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}
}