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mini1013/
master_cate_top_bt5_4

Name: master_cate_top_bt5_4
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
2	'에스쁘아 프로테일러 비글로우 스틱 파운데이션 13g 23호베이지 (#M)홈>화장품/미용>베이스메이크업>파운데이션>스틱형 Naverstore > 화장품/미용 > 베이스메이크업 > 파운데이션 > 스틱형' '그라펜 에어커버 스틱 파운데이션 23호 베이지 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 파운데이션 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 파운데이션' '바비 브라운 스킨 파운데이션 스틱-2.5 원 샌드 9g (#M)화장품/미용>베이스메이크업>파운데이션>크림형 Naverstore > 화장품/미용 > 베이스메이크업 > 파운데이션 > 크림형'
1	'정샘물 스킨 세팅 톤 코렉팅 베이스 40ml 글로잉 베이스 (#M)11st>메이크업>페이스메이크업>메이크업베이스 11st > 뷰티 > 메이크업 > 페이스메이크업 > 메이크업베이스' '아이오페 퍼펙트 커버 메이크업베이스 35ml 2호 라이트퍼플 × 3개 (#M)쿠팡 홈>뷰티>메이크업>베이스 메이크업>베이스/프라이머 Coupang > 뷰티 > 메이크업 > 베이스 메이크업 > 베이스/프라이머' '아이오페 퍼펙트 커버 베이스 35ml 2호-퍼플 (#M)홈>화장품/미용>베이스메이크업>메이크업베이스 Naverstore > 화장품/미용 > 베이스메이크업 > 메이크업베이스'
0	'헤라 글로우 래스팅 파운데이션 17C1 페탈 아이보리 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 베이스/프라이머 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 베이스/프라이머' '[에스티 로더] 더블웨어 파운데이션 30ml SPF 10/PA++ (+프라이머 정품 ) 1W0 웜 포슬린 홈>기획 세트;홈>더블웨어;홈>더블 웨어;화장품/미용>베이스메이크업>파운데이션>리퀴드형;(#M)홈>전체상품 Naverstore > 베이스메이크업 > 파운데이션' '에스쁘아 프로테일러 파운데이션 비 글로우 10ml 4호 베이지 × 1개 (#M)쿠팡 홈>뷰티>메이크업>베이스 메이크업>파운데이션 Coupang > 뷰티 > 로드샵 > 메이크업 > 베이스 메이크업 > 파운데이션'
4	'시세이도 스포츠 커버 파운데이션 20g S101 (#M)홈>화장품/미용>베이스메이크업>파운데이션>크림형 Naverstore > 화장품/미용 > 베이스메이크업 > 파운데이션 > 크림형' '시세이도 스포츠 커버 파운데이션 20g S100 × 1개 Coupang > 뷰티 > 메이크업 > 베이스 메이크업 > 파운데이션;(#M)쿠팡 홈>뷰티>메이크업>베이스 메이크업>파운데이션 Coupang > 뷰티 > 메이크업 > 베이스 메이크업 > 파운데이션' '에이지투웨니스 오리지날 샤이닝드롭 케이스+리필3개 (+커피쿠폰+폼20ml) 샤이닝드롭(화이트)23호케이스+리필3개_폼20ml (#M)화장품/미용>베이스메이크업>파운데이션>쿠션형 AD > Naverstore > 화장품/미용 > 베이스메이크업 > 파운데이션 > 크림형'
3	'매트 벨벳 스킨 컴팩트 스폰지 단품없음 LotteOn > 뷰티 > 뷰티기기 > 액세서리/소모품 LotteOn > 뷰티 > 뷰티기기 > 액세서리/소모품' '[BF적립] 엉크르 드 뽀 쿠션&리필 세트(+스탠딩 미러+5천LPOINT) 20호_15호 LOREAL > DepartmentLotteOn > 입생로랑 > Branded > 입생로랑 LOREAL > DepartmentLotteOn > 입생로랑 > Branded > 입생로랑' '코튼 LotteOn > 뷰티 > 뷰티기기 > 액세서리/소모품 LotteOn > 뷰티 > 뷰티기기 > 액세서리/소모품'

Evaluation

Metrics

Label	Accuracy
all	0.9475

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_bt5_4")
# Run inference
preds = model("[시세이도] NEW 싱크로 스킨 래디언트 리프팅 파운데이션 SPF30/PA++++ 30ml 130 오팔 (#M)홈>메이크업>베이스메이크업 HMALL > 뷰티 > 메이크업 > 베이스메이크업")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	12	22.928	52

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	0.521	-
0.1279	50	0.4636	-
0.2558	100	0.42	-
0.3836	150	0.292	-
0.5115	200	0.1539	-
0.6394	250	0.0626	-
0.7673	300	0.0343	-
0.8951	350	0.0071	-
1.0230	400	0.0023	-
1.1509	450	0.0005	-
1.2788	500	0.0006	-
1.4066	550	0.0003	-
1.5345	600	0.0002	-
1.6624	650	0.0001	-
1.7903	700	0.0002	-
1.9182	750	0.0006	-
2.0460	800	0.0002	-
2.1739	850	0.0001	-
2.3018	900	0.0	-
2.4297	950	0.0	-
2.5575	1000	0.0	-
2.6854	1050	0.0	-
2.8133	1100	0.0	-
2.9412	1150	0.0	-
3.0691	1200	0.0	-
3.1969	1250	0.0	-
3.3248	1300	0.0	-
3.4527	1350	0.0007	-
3.5806	1400	0.0005	-
3.7084	1450	0.0009	-
3.8363	1500	0.0008	-
3.9642	1550	0.0003	-
4.0921	1600	0.0002	-
4.2199	1650	0.0	-
4.3478	1700	0.0	-
4.4757	1750	0.0	-
4.6036	1800	0.0	-
4.7315	1850	0.0	-
4.8593	1900	0.0	-
4.9872	1950	0.0	-
5.1151	2000	0.0	-
5.2430	2050	0.0	-
5.3708	2100	0.0	-
5.4987	2150	0.0	-
5.6266	2200	0.0	-
5.7545	2250	0.0	-
5.8824	2300	0.0	-
6.0102	2350	0.0001	-
6.1381	2400	0.0006	-
6.2660	2450	0.0	-
6.3939	2500	0.0	-
6.5217	2550	0.0	-
6.6496	2600	0.0	-
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	0.0004	-
19.4373	7600	0.0028	-
19.5652	7650	0.0003	-
19.6931	7700	0.0002	-
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

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}
}