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mini1013
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master_item_bt_setfit

Text Classification
setfit
Safetensors
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results
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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:

  1. Fine-tuning a Sentence Transformer with contrastive learning.
  2. 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

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 -
7.8786 12850 0.0 -
7.9093 12900 0.0 -
7.9399 12950 0.0 -
7.9706 13000 0.0 -
8.0012 13050 0.0001 -
8.0319 13100 0.0 -
8.0625 13150 0.0001 -
8.0932 13200 0.0013 -
8.1239 13250 0.0005 -
8.1545 13300 0.0 -
8.1852 13350 0.0 -
8.2158 13400 0.0 -
8.2465 13450 0.0 -
8.2771 13500 0.0014 -
8.3078 13550 0.0 -
8.3384 13600 0.0 -
8.3691 13650 0.0003 -
8.3998 13700 0.0 -
8.4304 13750 0.0 -
8.4611 13800 0.0 -
8.4917 13850 0.0 -
8.5224 13900 0.0 -
8.5530 13950 0.0 -
8.5837 14000 0.0 -
8.6143 14050 0.0 -
8.6450 14100 0.0 -
8.6757 14150 0.0 -
8.7063 14200 0.0 -
8.7370 14250 0.0001 -
8.7676 14300 0.0 -
8.7983 14350 0.0 -
8.8289 14400 0.0 -
8.8596 14450 0.0 -
8.8903 14500 0.0 -
8.9209 14550 0.0 -
8.9516 14600 0.0 -
8.9822 14650 0.0005 -
9.0129 14700 0.0001 -
9.0435 14750 0.0001 -
9.0742 14800 0.0 -
9.1048 14850 0.0 -
9.1355 14900 0.0 -
9.1662 14950 0.0 -
9.1968 15000 0.0 -
9.2275 15050 0.0001 -
9.2581 15100 0.0 -
9.2888 15150 0.0 -
9.3194 15200 0.0 -
9.3501 15250 0.0 -
9.3807 15300 0.0 -
9.4114 15350 0.0 -
9.4421 15400 0.0 -
9.4727 15450 0.0 -
9.5034 15500 0.0 -
9.5340 15550 0.0 -
9.5647 15600 0.0 -
9.5953 15650 0.0 -
9.6260 15700 0.0009 -
9.6567 15750 0.0 -
9.6873 15800 0.0 -
9.7180 15850 0.0 -
9.7486 15900 0.0 -
9.7793 15950 0.0 -
9.8099 16000 0.0 -
9.8406 16050 0.0 -
9.8712 16100 0.0001 -
9.9019 16150 0.0 -
9.9326 16200 0.0007 -
9.9632 16250 0.0001 -
9.9939 16300 0.0002 -
10.0245 16350 0.0001 -
10.0552 16400 0.0 -
10.0858 16450 0.0 -
10.1165 16500 0.0 -
10.1471 16550 0.0 -
10.1778 16600 0.0003 -
10.2085 16650 0.0003 -
10.2391 16700 0.0 -
10.2698 16750 0.0001 -
10.3004 16800 0.0 -
10.3311 16850 0.001 -
10.3617 16900 0.0 -
10.3924 16950 0.0 -
10.4231 17000 0.0 -
10.4537 17050 0.0 -
10.4844 17100 0.0 -
10.5150 17150 0.0 -
10.5457 17200 0.0 -
10.5763 17250 0.0 -
10.6070 17300 0.0 -
10.6376 17350 0.0 -
10.6683 17400 0.0013 -
10.6990 17450 0.0 -
10.7296 17500 0.0 -
10.7603 17550 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}
}
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