setfit safetensors roberta sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:mini1013/master_domain base_model:finetune:mini1013/master_domain model-index region:us
---
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](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) 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](https://www.sbert.net) 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:** [mini1013/master_domain](https://huggingface.co/mini1013/master_domain)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 4 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)

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

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.6207   |

## Uses

### Direct Use for Inference

First install the SetFit library:

```bash
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_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")
```

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## 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
```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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