metadata

base_model: klue/roberta-base
library_name: setfit
metrics:
  - metric
pipeline_tag: text-classification
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: 올비고 천연 저자극 어성초 때비누 목욕 샤워 비누 어성초때비누 1개 (주) 솔루미랩
  - text: 폴미첼 XTG 왁스 100ml 엑스티지 11203582 옵션없음 그리드
  - text: 아요델 콜라겐 리프팅 아이크림 20ml 6개 옵션없음 건강드림
  - text: 존슨즈 콘스타치 파우더 피부 분칠 아기엉덩이 아기 옵션없음 에이치제이컴퍼니
  - text: '[NEW] 3CE 드롭 글로우 젤 3.8g (+글로시파우치) (도착보장) MILDER (주)난다'
inference: true
model-index:
  - name: SetFit with klue/roberta-base
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: metric
            value: 0.9036363636363637
            name: Metric

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: 13 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
12	'엘립스 헤어에센스 비타민 오일 바이탈리티 위드 진생 허니 오렌지 자 50ml 1022179 옵션없음 가이던스' '아모스 녹차실감 지성샴푸 500g 컬링2x에센스150g+컬링2x에센스38g 아모스 전문샵' '헤드앤숄더 쿨 멘솔 컨디셔닝 린스 850ml x 1개 옵션없음 지니인터네셔널 주식회사'
1	'[위글위글] 네일 발톱깎이 손톱깎이 세트 - Smile We Love Pink Smile We Love Pink 주식회사 아트쉐어' '3종 손톱깎이세트 택1 4W51DC511E C. 구름 케이스 화이트몰' '요고마요 YOGO 요고 망고비트 젤오프 비트 망고비트 젤오프_네일 비트홀더 케이스 증정 아이비티(IBT)'
0	'사임당 크린싱젤 120ml X 2개 (클린징 세안제) 옵션없음 바른스토어' '페리페라 스피디 브로우 오토 펜슬, 03호 브라운, 1개 옵션없음 플래너' '[랩시리즈](신세계 강남점)NEW 안티에이지 맥스 LS 워터로션 200ml 옵션없음 주식회사 에스에스지닷컴'
9	'비건이펙트 클린 앤 글로우 청보리 LHA 젤 클렌저 205ml 기획 (+토너패드 4eA ) 도매가능 옵션없음 앱스' 'S.NATURE 에스네이처 아쿠아 라이스 약산성 클렌징폼 160ml 8809506310680 259493 NONE 냥냥홀릭' '히스토랩 워터맥스 밀크 클렌저 1200ml 옵션없음 히트마켓'
6	'1/1+1 스틸 마스카라 내추럴 롱래쉬 볼륨 워터프루프 메탈 마스카라 01 블랙x2 와이우' '생로랑 GLOSS VOLUPTE LIPGLOSS 206 0.20 OZ BOX리스 와이프선물 옵션없음 남인터내셔널' '프롬메디 초고속 속눈썹영양제 하이퍼 큐어 래쉬 세럼 10ml 하이퍼 큐어 래쉬 세럼 1개 (주)에디스'
4	'헤라 메이크업 픽서 80ml 메이크업 고정 스프레이 옵션없음 (주) 성은' 'Candy doll 캔디돌 브라이트 퓨어 베이스 옵션없음 WORLD TRADING CO., LTD' '[국내매장판] 베네피트 프라이머 모공프라이머 더포어페셔널 모공 커버 지우개 7.5ml 프라이머 미니 + 슈퍼세터 미니 + 파우치 하이블랭크'
8	'[시효 17번 앰플] 한로 감국꽃 아이 링클 케어 앰플 20ml 옵션없음 주식회사 로시안' 'CEPOLAB 세포랩 바이오제닉 에센스 클렙스 오리지널 90% 30ml 옵션없음 주식회사 아워스' '호주산 포포크림 30g 3개입 멀티밤 파파야오일 옵션없음 코지(KOZZY)'
5	'필리밀리 코 쉐딩브러시 857 옵션없음 뉴베이스' '휴대용 화장품 소분 용기 여행용 공병 세트 샴푸 스프레이 거품 튜브 파스텔 핑크 친절한 이사장' '타투커버 컨실러 흉터 방송 타투 분장 가리기 문신 점 5. 자연색 2개 아바니'
7	'디보티드 크리에이션 포춘 브론저 태닝 로션 382.7g 13온스 옵션없음 비포유' '알롱 컨디셔닝 알로에젤 알로에 수딩젤 500ml 컨디셔닝 수딩젤 500ml 메리앤' '헤라 선 메이트 프로텍터 50ml 옵션없음 언더커버 빌리어네어'
3	'시어 버터 드라이 스킨 핸드 크림 150ml 옵션없음 뉴글로벌' '이탈왁스 하드 너바나 아로마틱스파 라벤더1kg 옵션없음 파인뷰티' '에바스 블루 로즈마인 샤워코롱 185ml O 옵션없음 와이케이비 (YKB) 상사'
10	'조 말론 라임 바질 앤 만다린 카 디퓨저 카트리지 1pc 261795 상품 상세설명 참조' '에르메스 트래블퍼퓸 3종세트 C 옵션없음 씨앤비코퍼레이션' '룸 디퓨저 코리앤더 200ml CL13965000200 투명_F 라부르켓(L:A BRUKET AB)/(주)신세계인터내셔날, 서울특별시 강남구 도산대로 449, 소비자상담실: 1644-4490'
11	'아모스 스타일 익스프레션 홀딩 글레이즈 300ml 옵션없음 정품몰' 'Hayashi 하야시 시스템 디자인 트리플 플레이 볼류마이징 무스 7oz x 2개 2개입 유럽기준' '새한 체리 미라클 피니쉬 수퍼하드 스프레이 240ml 옵션없음 도매백'
2	'[1+1] 물이 필요없는 디디에즈 병풀 겔 모델링팩 20회분+팩도구세트 병풀_머드 주식회사 예스나인' 'DIY 페인팅 코스프레 흰색 베니스 고양이 얼굴 종이 마스크, 도색되지 않은 10 개 옵션없음 글로젠' '베몽테스 엑소가 필러 모델링 마스크 10회분 피부 탄력 엑소가 필러 모델링 마스크 xtt 주식회사 스킨몽(Skinmong co.,ltd.)'

Evaluation

Metrics

Label	Metric
all	0.9036

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_item_bt_setfit")
# Run inference
preds = model("아요델 콜라겐 리프팅 아이크림 20ml 6개 옵션없음 건강드림")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	3	9.8015	33

Label	Training Sample Count
0	1229
1	559
2	654
3	1528
4	563
5	677
6	1157
7	563
8	1037
9	1034
10	219
11	544
12	671

Training Hyperparameters

batch_size: (512, 512)
num_epochs: (20, 20)
max_steps: -1
sampling_strategy: oversampling
num_iterations: 40
body_learning_rate: (2e-05, 2e-05)
head_learning_rate: 2e-05
loss: CosineSimilarityLoss
distance_metric: cosine_distance
margin: 0.25
end_to_end: False
use_amp: False
warmup_proportion: 0.1
seed: 42
eval_max_steps: -1
load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0006	1	0.3164	-
0.0307	50	0.3066	-
0.0613	100	0.2384	-
0.0920	150	0.226	-
0.1226	200	0.2162	-
0.1533	250	0.2202	-
0.1839	300	0.1973	-
0.2146	350	0.1818	-
0.2452	400	0.1629	-
0.2759	450	0.1734	-
0.3066	500	0.1624	-
0.3372	550	0.1435	-
0.3679	600	0.1433	-
0.3985	650	0.1259	-
0.4292	700	0.1175	-
0.4598	750	0.1201	-
0.4905	800	0.0958	-
0.5212	850	0.0938	-
0.5518	900	0.0784	-
0.5825	950	0.081	-
0.6131	1000	0.0673	-
0.6438	1050	0.0755	-
0.6744	1100	0.0498	-
0.7051	1150	0.0676	-
0.7357	1200	0.0474	-
0.7664	1250	0.0557	-
0.7971	1300	0.0384	-
0.8277	1350	0.0415	-
0.8584	1400	0.0415	-
0.8890	1450	0.0393	-
0.9197	1500	0.0333	-
0.9503	1550	0.0231	-
0.9810	1600	0.0162	-
1.0116	1650	0.024	-
1.0423	1700	0.0178	-
1.0730	1750	0.0175	-
1.1036	1800	0.0112	-
1.1343	1850	0.0109	-
1.1649	1900	0.0085	-
1.1956	1950	0.01	-
1.2262	2000	0.0076	-
1.2569	2050	0.0068	-
1.2876	2100	0.009	-
1.3182	2150	0.0066	-
1.3489	2200	0.0069	-
1.3795	2250	0.0034	-
1.4102	2300	0.0033	-
1.4408	2350	0.005	-
1.4715	2400	0.004	-
1.5021	2450	0.0014	-
1.5328	2500	0.0034	-
1.5635	2550	0.0026	-
1.5941	2600	0.003	-
1.6248	2650	0.0047	-
1.6554	2700	0.0019	-
1.6861	2750	0.0009	-
1.7167	2800	0.004	-
1.7474	2850	0.0006	-
1.7781	2900	0.0022	-
1.8087	2950	0.0033	-
1.8394	3000	0.0006	-
1.8700	3050	0.0021	-
1.9007	3100	0.0008	-
1.9313	3150	0.0037	-
1.9620	3200	0.0038	-
1.9926	3250	0.0013	-
2.0233	3300	0.0021	-
2.0540	3350	0.0008	-
2.0846	3400	0.0018	-
2.1153	3450	0.0011	-
2.1459	3500	0.0006	-
2.1766	3550	0.0003	-
2.2072	3600	0.0002	-
2.2379	3650	0.0002	-
2.2685	3700	0.0001	-
2.2992	3750	0.0003	-
2.3299	3800	0.0005	-
2.3605	3850	0.0027	-
2.3912	3900	0.0004	-
2.4218	3950	0.0018	-
2.4525	4000	0.0006	-
2.4831	4050	0.0002	-
2.5138	4100	0.0001	-
2.5445	4150	0.0008	-
2.5751	4200	0.0001	-
2.6058	4250	0.0002	-
2.6364	4300	0.0007	-
2.6671	4350	0.0002	-
2.6977	4400	0.0027	-
2.7284	4450	0.0002	-
2.7590	4500	0.0003	-
2.7897	4550	0.001	-
2.8204	4600	0.0001	-
2.8510	4650	0.0015	-
2.8817	4700	0.003	-
2.9123	4750	0.0002	-
2.9430	4800	0.0019	-
2.9736	4850	0.0018	-
3.0043	4900	0.0002	-
3.0349	4950	0.0001	-
3.0656	5000	0.001	-
3.0963	5050	0.0004	-
3.1269	5100	0.0004	-
3.1576	5150	0.0003	-
3.1882	5200	0.0008	-
3.2189	5250	0.0007	-
3.2495	5300	0.0008	-
3.2802	5350	0.0003	-
3.3109	5400	0.0006	-
3.3415	5450	0.0047	-
3.3722	5500	0.0019	-
3.4028	5550	0.0006	-
3.4335	5600	0.0002	-
3.4641	5650	0.0001	-
3.4948	5700	0.0001	-
3.5254	5750	0.0001	-
3.5561	5800	0.0001	-
3.5868	5850	0.0001	-
3.6174	5900	0.0014	-
3.6481	5950	0.0001	-
3.6787	6000	0.0002	-
3.7094	6050	0.0	-
3.7400	6100	0.0001	-
3.7707	6150	0.0002	-
3.8013	6200	0.0002	-
3.8320	6250	0.0017	-
3.8627	6300	0.0015	-
3.8933	6350	0.0008	-
3.9240	6400	0.0001	-
3.9546	6450	0.0003	-
3.9853	6500	0.0001	-
4.0159	6550	0.0	-
4.0466	6600	0.0005	-
4.0773	6650	0.0004	-
4.1079	6700	0.0	-
4.1386	6750	0.0001	-
4.1692	6800	0.0008	-
4.1999	6850	0.0001	-
4.2305	6900	0.0039	-
4.2612	6950	0.0001	-
4.2918	7000	0.0009	-
4.3225	7050	0.0005	-
4.3532	7100	0.0001	-
4.3838	7150	0.0009	-
4.4145	7200	0.0	-
4.4451	7250	0.0002	-
4.4758	7300	0.0	-
4.5064	7350	0.0	-
4.5371	7400	0.0	-
4.5677	7450	0.0	-
4.5984	7500	0.0	-
4.6291	7550	0.0	-
4.6597	7600	0.0	-
4.6904	7650	0.0005	-
4.7210	7700	0.0007	-
4.7517	7750	0.0	-
4.7823	7800	0.0	-
4.8130	7850	0.0005	-
4.8437	7900	0.0001	-
4.8743	7950	0.0	-
4.9050	8000	0.0	-
4.9356	8050	0.0001	-
4.9663	8100	0.0011	-
4.9969	8150	0.0001	-
5.0276	8200	0.0006	-
5.0582	8250	0.0018	-
5.0889	8300	0.0	-
5.1196	8350	0.0001	-
5.1502	8400	0.0001	-
5.1809	8450	0.0002	-
5.2115	8500	0.0	-
5.2422	8550	0.0004	-
5.2728	8600	0.0001	-
5.3035	8650	0.0	-
5.3342	8700	0.0	-
5.3648	8750	0.0001	-
5.3955	8800	0.0001	-
5.4261	8850	0.0001	-
5.4568	8900	0.0	-
5.4874	8950	0.0001	-
5.5181	9000	0.0015	-
5.5487	9050	0.0018	-
5.5794	9100	0.0001	-
5.6101	9150	0.0001	-
5.6407	9200	0.0015	-
5.6714	9250	0.0	-
5.7020	9300	0.0004	-
5.7327	9350	0.0001	-
5.7633	9400	0.0019	-
5.7940	9450	0.0019	-
5.8246	9500	0.0001	-
5.8553	9550	0.0001	-
5.8860	9600	0.0	-
5.9166	9650	0.0002	-
5.9473	9700	0.0001	-
5.9779	9750	0.0	-
6.0086	9800	0.0	-
6.0392	9850	0.0	-
6.0699	9900	0.0	-
6.1006	9950	0.0	-
6.1312	10000	0.0001	-
6.1619	10050	0.0	-
6.1925	10100	0.0	-
6.2232	10150	0.0003	-
6.2538	10200	0.0	-
6.2845	10250	0.0	-
6.3151	10300	0.0	-
6.3458	10350	0.0	-
6.3765	10400	0.0	-
6.4071	10450	0.0	-
6.4378	10500	0.0	-
6.4684	10550	0.0001	-
6.4991	10600	0.0	-
6.5297	10650	0.0001	-
6.5604	10700	0.0003	-
6.5910	10750	0.0	-
6.6217	10800	0.0	-
6.6524	10850	0.0	-
6.6830	10900	0.0	-
6.7137	10950	0.0	-
6.7443	11000	0.0	-
6.7750	11050	0.0	-
6.8056	11100	0.0001	-
6.8363	11150	0.0	-
6.8670	11200	0.0	-
6.8976	11250	0.0	-
6.9283	11300	0.0	-
6.9589	11350	0.0002	-
6.9896	11400	0.0006	-
7.0202	11450	0.0	-
7.0509	11500	0.0009	-
7.0815	11550	0.001	-
7.1122	11600	0.0003	-
7.1429	11650	0.0003	-
7.1735	11700	0.0	-
7.2042	11750	0.0	-
7.2348	11800	0.0	-
7.2655	11850	0.0	-
7.2961	11900	0.0001	-
7.3268	11950	0.0	-
7.3574	12000	0.0	-
7.3881	12050	0.0	-
7.4188	12100	0.0	-
7.4494	12150	0.0	-
7.4801	12200	0.0002	-
7.5107	12250	0.0	-
7.5414	12300	0.0	-
7.5720	12350	0.0001	-
7.6027	12400	0.0	-
7.6334	12450	0.0001	-
7.6640	12500	0.0	-
7.6947	12550	0.0	-
7.7253	12600	0.0	-
7.7560	12650	0.0	-
7.7866	12700	0.0	-
7.8173	12750	0.0	-
7.8479	12800	0.0	-
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Framework Versions

Python: 3.10.12
SetFit: 1.1.0.dev0
Sentence Transformers: 3.1.1
Transformers: 4.45.1
PyTorch: 2.4.0+cu121
Datasets: 2.20.0
Tokenizers: 0.20.0

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}
}