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Text Classification
setfit
Safetensors
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results
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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: 노트북 > msi > 블루라이트차단
  - text: 해외직구 > 건강식품 > 칼슘
  - text: 출산  /  육아용품 > 침구  /  수면용품 > 이불  /  담요
  - text: 생활가전 > 청소기 > 핸디청소기
  - text: 생활 > 건강  /  안마용품 > 온열  /  찜질용품 > 냉온주머니  /  핫팩
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: 0.9797794117647058
            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:

  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: 18 classes

Model Sources

Model Labels

Label Examples
10
  • '자동차용품 > 차량용전자기기 > 차량용가전 > 기타가전'
  • '금호타이어 > 마제스티9ta91 > 19인치'
  • '타이어 > 금호타이어 > 마제스티9ta91'
7
  • '전동레저 / 인라인 / 킥보드 > 인라인용품 > 인라인바퀴'
  • '프리모리 > 캠핑가방'
  • 'ssgcom > 자전거 > 스케이트 > 롤러 > 자전거잡화 > 기타자전거잡화'
4
  • '강아지사료 > 건식사료수입산'
  • '사료샘플'
  • '펫상품 > 펫9단'
3
  • '문구 / 오피스 > 사무용품전문관 > 사무용가구 / 수납 > 데스크정리소품 > 모니터받침대'
  • '완구취미 > 보드게임 > 학습카드게임'
  • 'ssgcom > 문구 > 미술용품 > 피규어 > 미술 > 제도용품 > 미술 > 화방 > 조소용품 > 구성 > 디자인'
11
  • '세탁기건조기세트 > 건조기키트'
  • '가전컴퓨터 > 모니터 > 모니터 > 일반모니터'
  • 'ssgcom > 세탁기 > 생활가전 > 청소기 > 청소기필터 > 액세서리'
12
  • '그립톡젤리'
  • 'xbox액세서리 > 기타'
  • '카메라렌즈조명악세서리 > zhiyun지윤텍'
8
  • '건강식품 > 혈행 / 눈건강 / 간건강 > 밀크씨슬'
  • 'jardin1984스마트스토어 > 브랜드관'
  • '식품 > 면 / 통조림 / 가공식품 > 즉석밥 / 간편조리 > 기타즉석식품'
5
  • '바디케어 > 바디워시 > 바디클렌저'
  • '스킨케어 > 팩 / 마스크 > 슬리핑팩'
  • 'ssgcom > 메이크업 > 치크메이크업 > 하이라이터'
6
  • 'ssgcom > 주방용품 > 냄비 / 솥 / 주전자 > 돌솥 / 가마솥'
  • '생활 / 건강 > 생활용품 > 주방 / 청소세제 > 유리세정제'
  • '생활용품 > 공구 / 철물 / diy > 전동 / 정밀공구 > 전기톱 / 직소 > 리벤토'
15
  • '남성패션 > 맨투맨 / 후드 / 티셔츠 > 반팔티셔츠'
  • '여성커리어 > 팬츠 > 데님'
  • '남성패션 > 팬츠 > 데님'
16
  • '브랜드패션 > 여성신발'
  • 'ssgcom > 가방 > 지갑 > 캐주얼가방 > 토트백'
  • '남성패션 > 브랜드신발'
1
  • '헬스 / 건강식품 > 건강 / 의료용품 > 자세교정 / 보호대 > 바른자세용품'
  • '헬스 / 건강식품 > 건강 / 의료용품 > 보호대 / 교정용품 > 건강보호대'
  • '헬스 / 건강식품 > 건강 / 의료용품 > 눈건강 / 렌즈관리 > 렌즈관리용품'
14
  • 'ssgcom > 유모차 > 실내용품 > 침구 > 수면용품 > 방수요 > 패드 > 매트'
  • 'ssgcom > 유아동신발 / 잡화 > 신발 > 샌들'
  • '유아동 > 출산 / 육아용품 > 유아전용세제 > 유아세탁세제'
2
  • 'ssgcom > 도서 > 국내도서 > 여행 > 취미 > 레저 > 악기 > 레저 > 스포츠'
  • '도서 / 음반 / dvd > 해외도서 > 취미 / 실용 / 스포츠 > 스포츠 / 아웃도어 > 개인스포츠'
  • 'ssgcom > 도서 > 국내도서 > 잡지 > 잡지기타'
17
  • 'tv쇼핑 > 가구 / 인테리어'
  • '생활잡화패션 > 인테리어소품'
  • '책상desk'
13
  • '전자담배기기 > 가변모드기기'
  • '전자담배기기 > 입호흡mtl'
  • 'lilstore스마트스토어'
9
  • 'ssgcom > 여행 > 해외패키지 > 중국 / 홍콩 / 하이난'
  • 'ssgcom > 여행 > 호텔 / 리조트 / 펜션 > 국내호텔 / 리조트'
  • 'ssgcom > 여행 > 해외패키지 > 유럽'
0
  • '여행 / 렌탈 / 금융 > 여행 / 숙박 / 항공권'
  • '여행 / 렌탈 / 금융 > 상품권 / 이용권'
  • 'ssgcom > 여행 > 내륙여행 / 입장권 > 워터파크 / 스키'

Evaluation

Metrics

Label Metric
all 0.9798

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("setfit_model_id")
# Run inference
preds = model("해외직구 > 건강식품 > 칼슘")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 1 7.8919 45
Label Training Sample Count
0 52
1 422
2 377
3 535
4 4826
5 4085
6 3868
7 3223
8 3998
9 19
10 887
11 22087
12 2307
13 113
14 1409
15 2267
16 2404
17 929

Training Hyperparameters

  • batch_size: (512, 512)
  • num_epochs: (10, 10)
  • max_steps: -1
  • sampling_strategy: oversampling
  • num_iterations: 20
  • 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
0.0002 1 0.2773 -
0.0119 50 0.2679 -
0.0238 100 0.2132 -
0.0357 150 0.1508 -
0.0476 200 0.1032 -
0.0595 250 0.0765 -
0.0714 300 0.0692 -
0.0833 350 0.0675 -
0.0951 400 0.05 -
0.1070 450 0.0564 -
0.1189 500 0.0408 -
0.1308 550 0.0309 -
0.1427 600 0.029 -
0.1546 650 0.0268 -
0.1665 700 0.0357 -
0.1784 750 0.0295 -
0.1903 800 0.0242 -
0.2022 850 0.026 -
0.2141 900 0.0225 -
0.2260 950 0.0266 -
0.2379 1000 0.0193 -
0.2498 1050 0.0179 -
0.2617 1100 0.0208 -
0.2735 1150 0.0238 -
0.2854 1200 0.0196 -
0.2973 1250 0.0126 -
0.3092 1300 0.0194 -
0.3211 1350 0.0124 -
0.3330 1400 0.0175 -
0.3449 1450 0.0163 -
0.3568 1500 0.0097 -
0.3687 1550 0.0083 -
0.3806 1600 0.0192 -
0.3925 1650 0.0078 -
0.4044 1700 0.012 -
0.4163 1750 0.0087 -
0.4282 1800 0.0123 -
0.4401 1850 0.0149 -
0.4520 1900 0.0113 -
0.4638 1950 0.0102 -
0.4757 2000 0.0075 -
0.4876 2050 0.0049 -
0.4995 2100 0.0132 -
0.5114 2150 0.0044 -
0.5233 2200 0.0061 -
0.5352 2250 0.0088 -
0.5471 2300 0.0103 -
0.5590 2350 0.0107 -
0.5709 2400 0.0111 -
0.5828 2450 0.0119 -
0.5947 2500 0.0044 -
0.6066 2550 0.0105 -
0.6185 2600 0.0056 -
0.6304 2650 0.0089 -
0.6422 2700 0.0062 -
0.6541 2750 0.0099 -
0.6660 2800 0.0047 -
0.6779 2850 0.015 -
0.6898 2900 0.0034 -
0.7017 2950 0.0061 -
0.7136 3000 0.0077 -
0.7255 3050 0.0097 -
0.7374 3100 0.0071 -
0.7493 3150 0.0062 -
0.7612 3200 0.0157 -
0.7731 3250 0.0026 -
0.7850 3300 0.0048 -
0.7969 3350 0.0039 -
0.8088 3400 0.0088 -
0.8206 3450 0.0011 -
0.8325 3500 0.0034 -
0.8444 3550 0.0031 -
0.8563 3600 0.0033 -
0.8682 3650 0.0117 -
0.8801 3700 0.0073 -
0.8920 3750 0.0047 -
0.9039 3800 0.0008 -
0.9158 3850 0.0062 -
0.9277 3900 0.0032 -
0.9396 3950 0.0033 -
0.9515 4000 0.0081 -
0.9634 4050 0.0123 -
0.9753 4100 0.0025 -
0.9872 4150 0.0078 -
0.9990 4200 0.0047 -
1.0109 4250 0.0027 -
1.0228 4300 0.0052 -
1.0347 4350 0.0064 -
1.0466 4400 0.0092 -
1.0585 4450 0.0034 -
1.0704 4500 0.0046 -
1.0823 4550 0.0071 -
1.0942 4600 0.0061 -
1.1061 4650 0.0043 -
1.1180 4700 0.0052 -
1.1299 4750 0.0029 -
1.1418 4800 0.001 -
1.1537 4850 0.0053 -
1.1656 4900 0.0029 -
1.1775 4950 0.0003 -
1.1893 5000 0.0012 -
1.2012 5050 0.0014 -
1.2131 5100 0.0021 -
1.2250 5150 0.0024 -
1.2369 5200 0.0015 -
1.2488 5250 0.0057 -
1.2607 5300 0.0037 -
1.2726 5350 0.0088 -
1.2845 5400 0.01 -
1.2964 5450 0.0059 -
1.3083 5500 0.0016 -
1.3202 5550 0.004 -
1.3321 5600 0.0022 -
1.3440 5650 0.0044 -
1.3559 5700 0.0084 -
1.3677 5750 0.0046 -
1.3796 5800 0.0043 -
1.3915 5850 0.0044 -
1.4034 5900 0.0051 -
1.4153 5950 0.0051 -
1.4272 6000 0.0048 -
1.4391 6050 0.0021 -
1.4510 6100 0.0041 -
1.4629 6150 0.0047 -
1.4748 6200 0.0048 -
1.4867 6250 0.0019 -
1.4986 6300 0.005 -
1.5105 6350 0.0001 -
1.5224 6400 0.0004 -
1.5343 6450 0.0012 -
1.5461 6500 0.0003 -
1.5580 6550 0.0042 -
1.5699 6600 0.0022 -
1.5818 6650 0.0021 -
1.5937 6700 0.0014 -
1.6056 6750 0.0002 -
1.6175 6800 0.0014 -
1.6294 6850 0.0057 -
1.6413 6900 0.0023 -
1.6532 6950 0.0024 -
1.6651 7000 0.0028 -
1.6770 7050 0.0017 -
1.6889 7100 0.0056 -
1.7008 7150 0.0003 -
1.7127 7200 0.0006 -
1.7245 7250 0.0055 -
1.7364 7300 0.0001 -
1.7483 7350 0.0071 -
1.7602 7400 0.0013 -
1.7721 7450 0.0021 -
1.7840 7500 0.0022 -
1.7959 7550 0.001 -
1.8078 7600 0.0075 -
1.8197 7650 0.0003 -
1.8316 7700 0.0004 -
1.8435 7750 0.0004 -
1.8554 7800 0.0023 -
1.8673 7850 0.0032 -
1.8792 7900 0.0021 -
1.8911 7950 0.0028 -
1.9029 8000 0.0031 -
1.9148 8050 0.002 -
1.9267 8100 0.0041 -
1.9386 8150 0.0027 -
1.9505 8200 0.0003 -
1.9624 8250 0.0062 -
1.9743 8300 0.0005 -
1.9862 8350 0.0044 -
1.9981 8400 0.0016 -
2.0100 8450 0.0002 -
2.0219 8500 0.0003 -
2.0338 8550 0.0021 -
2.0457 8600 0.0027 -
2.0576 8650 0.001 -
2.0695 8700 0.0004 -
2.0814 8750 0.0027 -
2.0932 8800 0.0003 -
2.1051 8850 0.0015 -
2.1170 8900 0.002 -
2.1289 8950 0.0005 -
2.1408 9000 0.0067 -
2.1527 9050 0.001 -
2.1646 9100 0.0024 -
2.1765 9150 0.0004 -
2.1884 9200 0.0038 -
2.2003 9250 0.0001 -
2.2122 9300 0.0048 -
2.2241 9350 0.0021 -
2.2360 9400 0.0031 -
2.2479 9450 0.0024 -
2.2598 9500 0.0006 -
2.2716 9550 0.007 -
2.2835 9600 0.0001 -
2.2954 9650 0.0018 -
2.3073 9700 0.0013 -
2.3192 9750 0.0059 -
2.3311 9800 0.0012 -
2.3430 9850 0.0028 -
2.3549 9900 0.0025 -
2.3668 9950 0.0006 -
2.3787 10000 0.0005 -
2.3906 10050 0.0001 -
2.4025 10100 0.0002 -
2.4144 10150 0.0009 -
2.4263 10200 0.0004 -
2.4382 10250 0.001 -
2.4500 10300 0.0003 -
2.4619 10350 0.0003 -
2.4738 10400 0.0026 -
2.4857 10450 0.0002 -
2.4976 10500 0.0045 -
2.5095 10550 0.0017 -
2.5214 10600 0.0002 -
2.5333 10650 0.0018 -
2.5452 10700 0.0001 -
2.5571 10750 0.0023 -
2.5690 10800 0.0013 -
2.5809 10850 0.0022 -
2.5928 10900 0.0036 -
2.6047 10950 0.0012 -
2.6166 11000 0.0028 -
2.6284 11050 0.0019 -
2.6403 11100 0.0001 -
2.6522 11150 0.0044 -
2.6641 11200 0.0012 -
2.6760 11250 0.0013 -
2.6879 11300 0.0001 -
2.6998 11350 0.0016 -
2.7117 11400 0.0037 -
2.7236 11450 0.0003 -
2.7355 11500 0.0004 -
2.7474 11550 0.0055 -
2.7593 11600 0.0002 -
2.7712 11650 0.0001 -
2.7831 11700 0.0006 -
2.7950 11750 0.0061 -
2.8069 11800 0.0007 -
2.8187 11850 0.0027 -
2.8306 11900 0.0022 -
2.8425 11950 0.0002 -
2.8544 12000 0.0022 -
2.8663 12050 0.0015 -
2.8782 12100 0.0003 -
2.8901 12150 0.001 -
2.9020 12200 0.0014 -
2.9139 12250 0.0001 -
2.9258 12300 0.0009 -
2.9377 12350 0.0007 -
2.9496 12400 0.0005 -
2.9615 12450 0.0004 -
2.9734 12500 0.0004 -
2.9853 12550 0.0026 -
2.9971 12600 0.0011 -
3.0090 12650 0.0019 -
3.0209 12700 0.0 -
3.0328 12750 0.0004 -
3.0447 12800 0.0004 -
3.0566 12850 0.0001 -
3.0685 12900 0.0003 -
3.0804 12950 0.0003 -
3.0923 13000 0.0015 -
3.1042 13050 0.0018 -
3.1161 13100 0.002 -
3.1280 13150 0.0018 -
3.1399 13200 0.0002 -
3.1518 13250 0.0003 -
3.1637 13300 0.0007 -
3.1755 13350 0.0002 -
3.1874 13400 0.0014 -
3.1993 13450 0.0026 -
3.2112 13500 0.0005 -
3.2231 13550 0.0015 -
3.2350 13600 0.0012 -
3.2469 13650 0.0029 -
3.2588 13700 0.0001 -
3.2707 13750 0.0001 -
3.2826 13800 0.0013 -
3.2945 13850 0.0021 -
3.3064 13900 0.0002 -
3.3183 13950 0.0014 -
3.3302 14000 0.0021 -
3.3421 14050 0.0011 -
3.3539 14100 0.0007 -
3.3658 14150 0.0015 -
3.3777 14200 0.0022 -
3.3896 14250 0.0 -
3.4015 14300 0.0008 -
3.4134 14350 0.0002 -
3.4253 14400 0.0002 -
3.4372 14450 0.002 -
3.4491 14500 0.0019 -
3.4610 14550 0.0018 -
3.4729 14600 0.0001 -
3.4848 14650 0.002 -
3.4967 14700 0.0003 -
3.5086 14750 0.0004 -
3.5205 14800 0.0003 -
3.5324 14850 0.0019 -
3.5442 14900 0.0005 -
3.5561 14950 0.0007 -
3.5680 15000 0.0023 -
3.5799 15050 0.0019 -
3.5918 15100 0.0002 -
3.6037 15150 0.002 -
3.6156 15200 0.0023 -
3.6275 15250 0.0019 -
3.6394 15300 0.0005 -
3.6513 15350 0.0001 -
3.6632 15400 0.0009 -
3.6751 15450 0.0003 -
3.6870 15500 0.0052 -
3.6989 15550 0.0058 -
3.7108 15600 0.0003 -
3.7226 15650 0.0011 -
3.7345 15700 0.003 -
3.7464 15750 0.0003 -
3.7583 15800 0.0001 -
3.7702 15850 0.0004 -
3.7821 15900 0.0004 -
3.7940 15950 0.0001 -
3.8059 16000 0.0009 -
3.8178 16050 0.002 -
3.8297 16100 0.0004 -
3.8416 16150 0.0001 -
3.8535 16200 0.0004 -
3.8654 16250 0.0001 -
3.8773 16300 0.0014 -
3.8892 16350 0.002 -
3.9010 16400 0.0023 -
3.9129 16450 0.002 -
3.9248 16500 0.0004 -
3.9367 16550 0.0002 -
3.9486 16600 0.0001 -
3.9605 16650 0.0007 -
3.9724 16700 0.0009 -
3.9843 16750 0.0002 -
3.9962 16800 0.0006 -
4.0081 16850 0.0001 -
4.0200 16900 0.0004 -
4.0319 16950 0.0014 -
4.0438 17000 0.0001 -
4.0557 17050 0.001 -
4.0676 17100 0.0003 -
4.0794 17150 0.0045 -
4.0913 17200 0.0039 -
4.1032 17250 0.0005 -
4.1151 17300 0.001 -
4.1270 17350 0.0019 -
4.1389 17400 0.0 -
4.1508 17450 0.0003 -
4.1627 17500 0.0007 -
4.1746 17550 0.0052 -
4.1865 17600 0.0002 -
4.1984 17650 0.0006 -
4.2103 17700 0.0001 -
4.2222 17750 0.0 -
4.2341 17800 0.0002 -
4.2460 17850 0.0003 -
4.2578 17900 0.0012 -
4.2697 17950 0.0005 -
4.2816 18000 0.0003 -
4.2935 18050 0.0031 -
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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}
}