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master_cate_bt_top2_test

Text Classification
setfit
Safetensors
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results
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master_cate_bt_top2_test / README.md
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metadata
base_model: mini1013/master_domain
library_name: setfit
metrics:
  - accuracy
pipeline_tag: text-classification
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: >-
      손톱스티커 워터데칼 신상 네일스티커 페디스티커젤네일스티 07_데일리C 네일 002_NB 866 LotteOn > 뷰티 > 네일 >
      네일스티커/네일팁 LotteOn > 뷰티 > 네일 > 네일스티커/네일팁
  - text: >-
      오피아이 인피니트 샤인 베이스코트  LotteOn > 뷰티 > 네일 > 네일관리기기 > 전동네일관리기 LotteOn > 뷰티 > 네일
      > 네일관리기기 > 전동네일관리기
  - text: >-
      [스킨알엑스][OPI][인피니트샤인]H009-Award for Best Nails go  LotteOn > 뷰티 > 네일 >
      네일관리기기 > 젤네일램프 LotteOn > 뷰티 > 네일 > 네일관리기기 > 젤네일램프
  - text: >-
      아리따움 모디 컬러 네일즈 6ml 09호 크러쉬핑크 (#M)홈>화장품/미용>네일케어>매니큐어 Naverstore > 화장품/미용 >
      네일케어 > 매니큐어
  - text: >-
      오피아이 인피니트 샤인2 매니큐어 N71 × 1개 LotteOn > 뷰티 > 네일 > 네일컬러 > 네일폴리쉬 LotteOn > 뷰티
      > 네일 > 네일컬러 > 네일폴리쉬
inference: true
model-index:
  - name: SetFit with mini1013/master_domain
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.6207243460764588
            name: Accuracy

SetFit with mini1013/master_domain

This is a SetFit model that can be used for Text Classification. This SetFit model uses mini1013/master_domain 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 Sources

Model Labels

Label Examples
3
  • '미용기기 네일스티커 쫀득하고 이쁘고 쉬운 그랑블루 (#M)SSG.COM/스킨케어/클렌징/클렌징오일 ssg > 뷰티 > 스킨케어 > 클렌징 > 클렌징오일'
  • 'DUW445476 네일아트 컬렉션 인조손톱 바네사 네일팁 34종 - 네일케어 디자인A10 LotteOn > 뷰티 > 네일케어 > 네일세트 > 네일세트 LotteOn > 뷰티 > 네일케어 > 네일세트 > 네일세트'
  • '데싱디바 매직프레스 로즈 체크 2009 매직프레스 로즈 체크 LotteOn > 뷰티 > 메이크업 > 립메이크업 > 립틴트 LotteOn > 뷰티 > 메이크업 > 립메이크업 > 립틴트'
0
  • '아이엑트리스 세라믹 오일푸셔 혼합색상 × 1개 LotteOn > 뷰티 > 네일 > 네일케어소품 LotteOn > 뷰티 > 네일 > 네일케어소품'
  • '오피아이 넌아세톤 리무버 빨강 30ml × 16개 LotteOn > 뷰티 > 네일 > 네일관리기기 > 전동네일관리기 LotteOn > 뷰티 > 네일 > 네일관리기기 > 전동네일관리기'
  • '오피아이 엑스퍼트 터치 젤네일 보라 리무버 450ml × 1개 LotteOn > 뷰티 > 네일 > 네일케어 > 네일리무버 LotteOn > 뷰티 > 네일 > 네일케어 > 네일리무버'
2
  • '럽스위치 네일 안티셉틱 손소독제 1000ml 1개 (#M)쿠팡 홈>뷰티>바디>핸드/풋/데오>핸드케어>손소독제 Coupang > 뷰티 > 바디 > 핸드/풋/데오 > 핸드케어 > 손소독제'
  • 'OPI 영양제 스트랭쓰너 15ml OPI 케어 Gel Break NTR05 - BARELY BEIGE LotteOn > 뷰티 > 네일 > 네일케어 > 큐티클케어 LotteOn > 뷰티 > 네일 > 네일케어 > 큐티클케어'
  • '닥터네일 딥 세럼 3.3ml 16개 (#M)쿠팡 홈>뷰티>네일>큐티클/영양>손톱강화/영양 Coupang > 뷰티 > 네일 > 큐티클/영양 > 손톱강화/영양'
1
  • '디올 베르니 449 당상트 (#M)위메프 > 뷰티 > 네일케어 > 큐티클/영양 > 큐티클 오일 위메프 > 뷰티 > 네일케어 > 큐티클/영양 > 큐티클 오일'
  • '아지아 스마트 아이 충전식 바리깡 블랙_JP-150 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품 > 속눈썹관리 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품 > 속눈썹관리'
  • '에코스타일러 프로페셔널 스타일링 젤 크리스탈 355ml × 1개 쿠팡 홈>선물스토어>생일>화장품>남성화장품>남성 헤어케어;Coupang > 뷰티 > 헤어 > 헤어스타일링 > 헤어젤;(#M)쿠팡 홈>생활용품>헤어/바디/세안>스타일링/케어/세트>헤어스타일링>헤어젤 Coupang > 뷰티 > 헤어 > 헤어스타일링 > 헤어젤'

Evaluation

Metrics

Label Accuracy
all 0.6207

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_cate_bt_top2_test")
# Run inference
preds = model("아리따움 모디 컬러 네일즈 6ml 09호 크러쉬핑크 (#M)홈>화장품/미용>네일케어>매니큐어 Naverstore > 화장품/미용 > 네일케어 > 매니큐어")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 13 22.63 41
Label Training Sample Count
0 50
1 50
2 50
3 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
0.0032 1 0.4439 -
0.1597 50 0.4539 -
0.3195 100 0.4205 -
0.4792 150 0.3968 -
0.6390 200 0.3406 -
0.7987 250 0.2811 -
0.9585 300 0.2458 -
1.1182 350 0.2219 -
1.2780 400 0.2013 -
1.4377 450 0.2022 -
1.5974 500 0.1964 -
1.7572 550 0.1879 -
1.9169 600 0.1804 -
2.0767 650 0.138 -
2.2364 700 0.0945 -
2.3962 750 0.0722 -
2.5559 800 0.0619 -
2.7157 850 0.0396 -
2.8754 900 0.0264 -
3.0351 950 0.0213 -
3.1949 1000 0.0144 -
3.3546 1050 0.0112 -
3.5144 1100 0.0114 -
3.6741 1150 0.0151 -
3.8339 1200 0.0142 -
3.9936 1250 0.0141 -
4.1534 1300 0.0115 -
4.3131 1350 0.0123 -
4.4728 1400 0.0119 -
4.6326 1450 0.0122 -
4.7923 1500 0.0143 -
4.9521 1550 0.0126 -
5.1118 1600 0.0113 -
5.2716 1650 0.011 -
5.4313 1700 0.0145 -
5.5911 1750 0.0107 -
5.7508 1800 0.0136 -
5.9105 1850 0.0124 -
6.0703 1900 0.0136 -
6.2300 1950 0.0123 -
6.3898 2000 0.0151 -
6.5495 2050 0.0128 -
6.7093 2100 0.0106 -
6.8690 2150 0.0122 -
7.0288 2200 0.0135 -
7.1885 2250 0.0118 -
7.3482 2300 0.0101 -
7.5080 2350 0.0078 -
7.6677 2400 0.0064 -
7.8275 2450 0.006 -
7.9872 2500 0.0067 -
8.1470 2550 0.0055 -
8.3067 2600 0.0057 -
8.4665 2650 0.0077 -
8.6262 2700 0.0043 -
8.7859 2750 0.0065 -
8.9457 2800 0.0086 -
9.1054 2850 0.0059 -
9.2652 2900 0.0063 -
9.4249 2950 0.0053 -
9.5847 3000 0.0073 -
9.7444 3050 0.0056 -
9.9042 3100 0.0075 -
10.0639 3150 0.007 -
10.2236 3200 0.0049 -
10.3834 3250 0.0053 -
10.5431 3300 0.0016 -
10.7029 3350 0.0 -
10.8626 3400 0.0 -
11.0224 3450 0.0002 -
11.1821 3500 0.0031 -
11.3419 3550 0.0057 -
11.5016 3600 0.0029 -
11.6613 3650 0.0006 -
11.8211 3700 0.0 -
11.9808 3750 0.0004 -
12.1406 3800 0.0 -
12.3003 3850 0.0 -
12.4601 3900 0.0 -
12.6198 3950 0.0 -
12.7796 4000 0.0 -
12.9393 4050 0.0 -
13.0990 4100 0.0 -
13.2588 4150 0.0 -
13.4185 4200 0.0 -
13.5783 4250 0.0 -
13.7380 4300 0.0 -
13.8978 4350 0.0 -
14.0575 4400 0.0 -
14.2173 4450 0.0012 -
14.3770 4500 0.0004 -
14.5367 4550 0.0002 -
14.6965 4600 0.0 -
14.8562 4650 0.0 -
15.0160 4700 0.0001 -
15.1757 4750 0.0005 -
15.3355 4800 0.0001 -
15.4952 4850 0.0 -
15.6550 4900 0.0001 -
15.8147 4950 0.0 -
15.9744 5000 0.0 -
16.1342 5050 0.0 -
16.2939 5100 0.0 -
16.4537 5150 0.0 -
16.6134 5200 0.0 -
16.7732 5250 0.0 -
16.9329 5300 0.0 -
17.0927 5350 0.0 -
17.2524 5400 0.0 -
17.4121 5450 0.0 -
17.5719 5500 0.0 -
17.7316 5550 0.0 -
17.8914 5600 0.0 -
18.0511 5650 0.0 -
18.2109 5700 0.0 -
18.3706 5750 0.0 -
18.5304 5800 0.0 -
18.6901 5850 0.0 -
18.8498 5900 0.0 -
19.0096 5950 0.0 -
19.1693 6000 0.0 -
19.3291 6050 0.0 -
19.4888 6100 0.0 -
19.6486 6150 0.0 -
19.8083 6200 0.0 -
19.9681 6250 0.0 -
20.1278 6300 0.0 -
20.2875 6350 0.0 -
20.4473 6400 0.0 -
20.6070 6450 0.0 -
20.7668 6500 0.0 -
20.9265 6550 0.0 -
21.0863 6600 0.0 -
21.2460 6650 0.0 -
21.4058 6700 0.0 -
21.5655 6750 0.0 -
21.7252 6800 0.0 -
21.8850 6850 0.0 -
22.0447 6900 0.0 -
22.2045 6950 0.0 -
22.3642 7000 0.0 -
22.5240 7050 0.0 -
22.6837 7100 0.0 -
22.8435 7150 0.0 -
23.0032 7200 0.0 -
23.1629 7250 0.0 -
23.3227 7300 0.0 -
23.4824 7350 0.0 -
23.6422 7400 0.0 -
23.8019 7450 0.0 -
23.9617 7500 0.0 -
24.1214 7550 0.0 -
24.2812 7600 0.0 -
24.4409 7650 0.0 -
24.6006 7700 0.0 -
24.7604 7750 0.0 -
24.9201 7800 0.0 -
25.0799 7850 0.0 -
25.2396 7900 0.0 -
25.3994 7950 0.0 -
25.5591 8000 0.0 -
25.7188 8050 0.0 -
25.8786 8100 0.0 -
26.0383 8150 0.0 -
26.1981 8200 0.0 -
26.3578 8250 0.0 -
26.5176 8300 0.0 -
26.6773 8350 0.0 -
26.8371 8400 0.0 -
26.9968 8450 0.0 -
27.1565 8500 0.0 -
27.3163 8550 0.0 -
27.4760 8600 0.0 -
27.6358 8650 0.0 -
27.7955 8700 0.0 -
27.9553 8750 0.0 -
28.1150 8800 0.0 -
28.2748 8850 0.0 -
28.4345 8900 0.0 -
28.5942 8950 0.0 -
28.7540 9000 0.0 -
28.9137 9050 0.0 -
29.0735 9100 0.0 -
29.2332 9150 0.0 -
29.3930 9200 0.0 -
29.5527 9250 0.0 -
29.7125 9300 0.0 -
29.8722 9350 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 

@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}
}