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mini1013/
master_item_fi

Name: master_item_fi
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: 17 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
0.0	'축사 난방 판넬 송아지 애완견 개집 전기 열선 가열 -미디엄 가구/인테리어>DIY자재/용품>바닥재>필름난방' '냉장고 시트지 리폼 필름지 싱크대 에어컨 더블 도어 스티커 가구/인테리어>DIY자재/용품>시트지>단색시트지' '이코리브 사각형 전선 정리 몰딩 쫄대 1M 1P 학원 가구/인테리어>DIY자재/용품>몰딩'
14.0	'홈메이드 보드라운 비정형 러그 거실 카페트 매트 캐시미어 82번 LINE 가구/인테리어>카페트/러그>러그' '테라스꾸미기 캠핑용매트 발판매트 월컴 가구/인테리어>카페트/러그>발매트' '러그 발매트 화장실 현관 침실 주방 주방매트 대형 미끄럼방지 캠핑 PVC 가구/인테리어>카페트/러그>발매트'
9.0	'소화기 안내 아크릴 디자인 표지판 문패 가구/인테리어>인테리어소품>디자인문패' '우체통 유럽 철재 전원주택 스탠드 우체통 인테리어 -1 2m 레터 박스 A 가구/인테리어>인테리어소품>우체통' '빈티지 우체통 빨간 벽걸이 엔틱 카페 우편함 가구/인테리어>인테리어소품>우체통'
13.0	'베스트슬립 M2 올가닉 메달리스트 매트리스 K 가구/인테리어>침실가구>매트리스>킹매트리스' '화장대 의자 골드가구 등받이 인테리어 화장대 의자 가구/인테리어>침실가구>화장대>화장대의자' '플레인홈 포렐 모던 거실 협탁 580 가구/인테리어>침실가구>협탁'
6.0	'패딩 리본 키링 2컬러 가방꾸미기 가꾸 뜨개가방 부자재 가구/인테리어>수예>뜨개질>패키지' '어린이집 신체활동 공룡도안 전신 안전거울 아기안전거울 아기 가구/인테리어>수예>퀼트/펠트>도안' '5색재봉실 레인보우 컬러 바느질 퀼트 자수 원사 재봉 십자수 공예 가구/인테리어>수예>자수>실/바늘'
5.0	'다용도실 베란다 창고 정리 선반 세탁실 선반장 사무실 철제 키큰 가구/인테리어>수납가구>선반' '도서 잡지 사무실 분류 보관함 회전 신문 홍보물 전단지 스탠딩 가구/인테리어>수납가구>잡지꽂이' '프랑코홈 프랑코 리빙박스 62L 2개 SET 대용량 플라스틱 수납 정리함 FRANCO 가구/인테리어>수납가구>공간박스'
11.0	'비비엔다 포르페 냉감 아이싱 맥스 아기 쿨패드 유아특대형 슈퍼싱글 퀸 킹 가구/인테리어>침구단품>패드>싱글/슈퍼싱글패드' '매트리스커버높은 방수 토퍼 매트리스 커버 싱글 퀸 가구/인테리어>침구단품>매트/침대커버>싱글/슈퍼싱글침대커버' '모던하우스 리아 소프트워싱 차렵이불 Q 가구/인테리어>침구단품>차렵이불'
1.0	'데코라인 본테 높은 600 피규어 진열장 DHP006 가구/인테리어>거실가구>장식장' '현대의료기 모짜르트 4인 SF 카우치 컴포트 황토숯볼 흙소파 가구/인테리어>거실가구>소파>흙/돌소파' '오브민 접이식 폴딩 이동식 노트북 책상800 소파 사이드테이블 가구/인테리어>거실가구>테이블>사이드테이블'
4.0	'쿠션 던지기 리넨 베개 커버 귀여운 눈송이 케이스 새해 장식 가구/인테리어>솜류>쿠션솜' '바디필로우 대 롱쿠션 캔디솜 더크린 가구/인테리어>솜류>쿠션솜' '힐튼 퀼팅베개 계절베개 숙면베개 필로우 29859062 가구/인테리어>솜류>베개솜/속통>거위털/오리털베개솜'
16.0	'안고자는인형 대형 고양이 롱 바디필로우 수면 쿠션 모찌 찰떡 가구/인테리어>홈데코>쿠션/방석>대쿠션/대방석' '냉감 애견패드 강아지 고양이 쿨매트 듀라론 2P 눕자 40X60 가구/인테리어>홈데코>쿠션/방석>대쿠션/대방석' '3개 홀더 스마트폰 bob 거치대 썬바이저 차량용 자동차 가구/인테리어>홈데코>쿠션/방석>팔꿈치/손목쿠션'
3.0	'프린터거치대 프린터선반 다이 수납장 테이블 선반 -블랙 3단프레임 가구/인테리어>서재/사무용가구>책상>책상소품' '카파맥스 플러스 3단 책꽂이 연두 가구/인테리어>서재/사무용가구>책꽂이' '포밍 테이블 1800 사무용 회의실 책상 다용도 작업대 가구/인테리어>서재/사무용가구>사무/교구용가구>회의테이블'
8.0	'수영장 썬베드 야외 의자 비치 해변 호텔 펜션 테라스 리조트 침대 가구/인테리어>아웃도어가구>야외의자' '평벤치 학교 의자 공원 운동장 야외 휴게소 병원 벤 -1 8m U자형 가구/인테리어>아웃도어가구>야외벤치' '야외조립창고 컨테이너 농막수납장 공구보관함 잡화 유형 A 가구/인테리어>아웃도어가구>기타아웃도어가구'
7.0	'귀여운 로봇 낮은수납장 깊은 정리 이동식 3단 틈새 가구/인테리어>아동/주니어가구>수납장' '엘레아 메리다 800 5단 서랍장 가구/인테리어>아동/주니어가구>서랍장' '장바구니 일룸 에디키즈 장난감 정리대 가구/인테리어>아동/주니어가구>수납장'
15.0	'창안애 브러쉬 25mm 알루미늄 블라인드 30 x 30 가구/인테리어>커튼/블라인드>블라인드' '광목로만쉐이드 작은 동물 커튼 욕실 3D 디지털 침실 구름로만쉐이드 가구/인테리어>커튼/블라인드>로만셰이드' '에그박스 2단 슬라이딩 가구/인테리어>커튼/블라인드>로만셰이드'
12.0	'마이하우스 알러지케어 스타패치 핑크 키즈 차렵이불 풀세트 S/SS 가구/인테리어>침구세트>매트커버세트>슈퍼싱글매트커버세트' '올리비아데코 베리메리 60수 아사 요차렵세트 Q 가구/인테리어>침구세트>요이불세트>2/3인용' '레노마홈 스마일워싱 차렵이불 베개세트 Q 사계절 가구/인테리어>침구세트>이불베개세트>슈퍼싱글이불베개세트'
2.0	'슬립앤슬립 마스터유닛2 5분할 베개 소프트 LE1215376644 가구/인테리어>베개>메모리폼베개' '일자목베개 쿨젤 목편한 숙면 기능성 쿨링 메모리폼 여름 베개 1개 가구/인테리어>베개>메모리폼베개' '스패로우 스프링 필로우 메모리폼 베개 가구/인테리어>베개>메모리폼베개'
10.0	'라자가구 위드 고메 1600 주방수납장 세트 홈카페형 nk008 가구/인테리어>주방가구>주방수납장' '레트로하우스 코케 고무나무 원목 접이식 확장형 식탁 테이블 1600 가구/인테리어>주방가구>식탁/의자>식탁테이블' 'UNKNOWN 호환 리빙코리아 리빙웰 프리미엄 오븐 석쇠 OV250 가구/인테리어>주방가구>기타주방가구'

Evaluation

Metrics

Label	Accuracy
all	1.0

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_fi")
# Run inference
preds = model("클레마티스 오로라화병 가구/인테리어>인테리어소품>화병")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	2	8.9412	24

Label	Training Sample Count
0.0	980
1.0	280
2.0	133
3.0	350
4.0	349
5.0	840
6.0	490
7.0	893
8.0	420
9.0	1516
10.0	420
11.0	890
12.0	270
13.0	629
14.0	402
15.0	700
16.0	210

Training Hyperparameters

batch_size: (256, 256)
num_epochs: (30, 30)
max_steps: -1
sampling_strategy: oversampling
num_iterations: 50
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
0.0005	1	0.4481	-
0.0262	50	0.4495	-
0.0524	100	0.426	-
0.0786	150	0.3955	-
0.1048	200	0.3284	-
0.1310	250	0.2654	-
0.1572	300	0.209	-
0.1833	350	0.1354	-
0.2095	400	0.0875	-
0.2357	450	0.0572	-
0.2619	500	0.0427	-
0.2881	550	0.0289	-
0.3143	600	0.0222	-
0.3405	650	0.0143	-
0.3667	700	0.0101	-
0.3929	750	0.0092	-
0.4191	800	0.0068	-
0.4453	850	0.0066	-
0.4715	900	0.0046	-
0.4976	950	0.0049	-
0.5238	1000	0.0046	-
0.5500	1050	0.0039	-
0.5762	1100	0.0038	-
0.6024	1150	0.0034	-
0.6286	1200	0.0029	-
0.6548	1250	0.0017	-
0.6810	1300	0.0011	-
0.7072	1350	0.0009	-
0.7334	1400	0.0006	-
0.7596	1450	0.0005	-
0.7858	1500	0.0004	-
0.8119	1550	0.0004	-
0.8381	1600	0.0003	-
0.8643	1650	0.0003	-
0.8905	1700	0.0003	-
0.9167	1750	0.0002	-
0.9429	1800	0.0002	-
0.9691	1850	0.0002	-
0.9953	1900	0.0002	-
1.0215	1950	0.0002	-
1.0477	2000	0.0002	-
1.0739	2050	0.0002	-
1.1001	2100	0.0001	-
1.1262	2150	0.0001	-
1.1524	2200	0.0001	-
1.1786	2250	0.0001	-
1.2048	2300	0.0001	-
1.2310	2350	0.0001	-
1.2572	2400	0.0001	-
1.2834	2450	0.0001	-
1.3096	2500	0.0001	-
1.3358	2550	0.0001	-
1.3620	2600	0.0001	-
1.3882	2650	0.0001	-
1.4144	2700	0.0001	-
1.4405	2750	0.0001	-
1.4667	2800	0.0001	-
1.4929	2850	0.0001	-
1.5191	2900	0.0001	-
1.5453	2950	0.0001	-
1.5715	3000	0.0001	-
1.5977	3050	0.0001	-
1.6239	3100	0.0001	-
1.6501	3150	0.0001	-
1.6763	3200	0.0	-
1.7025	3250	0.0001	-
1.7287	3300	0.0	-
1.7548	3350	0.0	-
1.7810	3400	0.0	-
1.8072	3450	0.0	-
1.8334	3500	0.0	-
1.8596	3550	0.0	-
1.8858	3600	0.0	-
1.9120	3650	0.0	-
1.9382	3700	0.0	-
1.9644	3750	0.0	-
1.9906	3800	0.0	-
2.0168	3850	0.0	-
2.0430	3900	0.0	-
2.0691	3950	0.0	-
2.0953	4000	0.0	-
2.1215	4050	0.0	-
2.1477	4100	0.0	-
2.1739	4150	0.0	-
2.2001	4200	0.0	-
2.2263	4250	0.0	-
2.2525	4300	0.0	-
2.2787	4350	0.0	-
2.3049	4400	0.0	-
2.3311	4450	0.0	-
2.3573	4500	0.0	-
2.3834	4550	0.0	-
2.4096	4600	0.0	-
2.4358	4650	0.0	-
2.4620	4700	0.0	-
2.4882	4750	0.0	-
2.5144	4800	0.0	-
2.5406	4850	0.0	-
2.5668	4900	0.0	-
2.5930	4950	0.0	-
2.6192	5000	0.0	-
2.6454	5050	0.0	-
2.6716	5100	0.0	-
2.6977	5150	0.0	-
2.7239	5200	0.0	-
2.7501	5250	0.0	-
2.7763	5300	0.0	-
2.8025	5350	0.0	-
2.8287	5400	0.0	-
2.8549	5450	0.0	-
2.8811	5500	0.0	-
2.9073	5550	0.0	-
2.9335	5600	0.0	-
2.9597	5650	0.0	-
2.9859	5700	0.0	-
3.0120	5750	0.0	-
3.0382	5800	0.0	-
3.0644	5850	0.0	-
3.0906	5900	0.0	-
3.1168	5950	0.0	-
3.1430	6000	0.0	-
3.1692	6050	0.0	-
3.1954	6100	0.0	-
3.2216	6150	0.0	-
3.2478	6200	0.0	-
3.2740	6250	0.0	-
3.3002	6300	0.0	-
3.3263	6350	0.0	-
3.3525	6400	0.0	-
3.3787	6450	0.0	-
3.4049	6500	0.0	-
3.4311	6550	0.0	-
3.4573	6600	0.0	-
3.4835	6650	0.0	-
3.5097	6700	0.0	-
3.5359	6750	0.0	-
3.5621	6800	0.0	-
3.5883	6850	0.0	-
3.6145	6900	0.0	-
3.6406	6950	0.0	-
3.6668	7000	0.0	-
3.6930	7050	0.0	-
3.7192	7100	0.0	-
3.7454	7150	0.0	-
3.7716	7200	0.0	-
3.7978	7250	0.0	-
3.8240	7300	0.0	-
3.8502	7350	0.0	-
3.8764	7400	0.0	-
3.9026	7450	0.0	-
3.9288	7500	0.0	-
3.9550	7550	0.0	-
3.9811	7600	0.0	-
4.0073	7650	0.0	-
4.0335	7700	0.0	-
4.0597	7750	0.0	-
4.0859	7800	0.0	-
4.1121	7850	0.0	-
4.1383	7900	0.0	-
4.1645	7950	0.0	-
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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}
}