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: 1분완성 네일팁 모음인조손톱 인조팁 붙이는네일팁 웨딩네 13)샤인네일팁-화이트 LotteOn > 뷰티 > 네일 > 네일스티커/네일팁
    LotteOn > 뷰티 > 네일 > 네일스티커/네일팁
- text: 오피아이 인피니트샤인2 매니큐어 MI12 × 1개 (#M)쿠팡 홈>뷰티>네일>일반네일>컬러 매니큐어 Coupang > 뷰티 > 네일
    > 일반네일 > 컬러 매니큐어
- text: 오피아이 젤 네일 컬러 GCV33 x 1개 (#M)쿠팡 홈>뷰티>네일>일반네일>컬러 매니큐어 Coupang > 뷰티 > 네일 > 일반네일
    > 컬러 매니큐어
- text: 디올 베르니 212 튀튀 LotteOn > 뷰티 > 메이크업 > 메이크업세트 LotteOn > 뷰티 > 메이크업 > 메이크업세트
- text: OPI 인피니트샤인 HRL31 LETS BE FRIENDS HRL31 - LETS BE FRIENDS! LotteOn > 뷰티 > 헤어/바디
    > 헤어스타일링 > 염색/매니큐어 LotteOn > 뷰티 > 헤어/바디 > 헤어스타일링 > 염색/매니큐어
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: 0.5301810865191147
      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>'네일팁 실크익스텐션 311160L1720771597 티타늄금 물방울 （풀값 ） LotteOn > 뷰티 > 네일케어 > 네일케어도구 > 손톱깎이 LotteOn > 뷰티 > 네일케어 > 네일케어도구 > 손톱깎이'</li><li>'엔비베베 어린이 화장품 선물세트 어린이 썬쿠션+키즈네일스티커+워시패드 1개 (#M)쿠팡 홈>뷰티>어린이화장품>세트/키트 Coupang > 뷰티 > 어린이화장품 > 세트/키트'</li><li>'래쉬톡 원터치 인조 속눈썹 섹시 걸 × 3개입 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품 > 속눈썹관리 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품 > 속눈썹관리'</li></ul>                                                  |
| 0     | <ul><li>'오피아이 넌아세톤 리무버 빨강 30ml × 5개 (#M)쿠팡 홈>뷰티>네일>일반네일>리무버 Coupang > 뷰티 > 네일 > 일반네일 > 리무버'</li><li>'[OPI][리무버] 넌아세톤리무버 30ml  ssg > 뷰티 > 메이크업 > 네일 ssg > 뷰티 > 메이크업 > 네일'</li><li>'포먼트 젤네일 O.4 블러쉬 뷰티 × 1개 (#M)쿠팡 홈>뷰티>네일>젤네일>컬러 젤 Coupang > 뷰티 > 네일 > 젤네일 > 컬러 젤'</li></ul>                                                                                                                                                 |
| 2     | <ul><li>'오피아이 프로스파 오일투고 큐티클 오일2197877 1 7.5ml x 1개2197877 1 (#M)SSG.COM/메이크업/베이스메이크업/컨실러 ssg > 뷰티 > 메이크업 > 베이스메이크업 > 컨실러'</li><li>'구찌 뷰티 [구찌] 베르니 아 옹글 하이 샤인 네일 라커 712 멜린다 그린 × 선택완료 (#M)쿠팡 홈>뷰티>네일>일반네일>컬러 매니큐어 Coupang > 뷰티 > 네일 > 일반네일 > 컬러 매니큐어'</li><li>'OPI ProSpa 각질 제거 큐티클 크림, 27ml  SSG.COM/메이크업/베이스메이크업/메이크업베이스;ssg > 뷰티 > 메이크업 > 베이스메이크업 > 메이크업베이스 ssg > 뷰티 > 메이크업 > 베이스메이크업 > 메이크업베이스'</li></ul>                |
| 1     | <ul><li>'르 베르니 루쥬 느와르 DepartmentLotteOn > 뷰티 > 헤어/바디 > 핸드/풋케어 > 네일케어 DepartmentLotteOn > 뷰티 > 헤어/바디 > 핸드/풋케어 > 네일케어'</li><li>'베씨 베이스젤 + 탑젤 + 지브라파일 2p 세트 베이스젤, 탑젤, 지브라파일(100/150) × 1세트 LotteOn > 뷰티 > 네일 > 네일아트소품 LotteOn > 뷰티 > 네일 > 네일아트소품'</li><li>'OPI OPI Chrome Effects Nail Lacquer Top Coat CPT31 - 0.5 oz 상세내용참조 × 상세내용참조 (#M)쿠팡 홈>뷰티>메이크업>베이스 메이크업>베이스/프라이머 Coupang > 뷰티 > 메이크업 > 베이스 메이크업 > 베이스/프라이머'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.5302   |

## 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_bt1_test_flat_top_cate")
# Run inference
preds = model("디올 베르니 212 튀튀 LotteOn > 뷰티 > 메이크업 > 메이크업세트 LotteOn > 뷰티 > 메이크업 > 메이크업세트")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 13  | 22.7236 | 41  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 49                    |
| 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    | 0.4603        | -               |
| 0.1608  | 50   | 0.4502        | -               |
| 0.3215  | 100  | 0.4315        | -               |
| 0.4823  | 150  | 0.3996        | -               |
| 0.6431  | 200  | 0.365         | -               |
| 0.8039  | 250  | 0.2954        | -               |
| 0.9646  | 300  | 0.2647        | -               |
| 1.1254  | 350  | 0.2378        | -               |
| 1.2862  | 400  | 0.2257        | -               |
| 1.4469  | 450  | 0.2165        | -               |
| 1.6077  | 500  | 0.213         | -               |
| 1.7685  | 550  | 0.1999        | -               |
| 1.9293  | 600  | 0.1838        | -               |
| 2.0900  | 650  | 0.1614        | -               |
| 2.2508  | 700  | 0.1164        | -               |
| 2.4116  | 750  | 0.0553        | -               |
| 2.5723  | 800  | 0.0366        | -               |
| 2.7331  | 850  | 0.0279        | -               |
| 2.8939  | 900  | 0.0219        | -               |
| 3.0547  | 950  | 0.0166        | -               |
| 3.2154  | 1000 | 0.0111        | -               |
| 3.3762  | 1050 | 0.0067        | -               |
| 3.5370  | 1100 | 0.0084        | -               |
| 3.6977  | 1150 | 0.0066        | -               |
| 3.8585  | 1200 | 0.0048        | -               |
| 4.0193  | 1250 | 0.0028        | -               |
| 4.1801  | 1300 | 0.0005        | -               |
| 4.3408  | 1350 | 0.0003        | -               |
| 4.5016  | 1400 | 0.0004        | -               |
| 4.6624  | 1450 | 0.0001        | -               |
| 4.8232  | 1500 | 0.0001        | -               |
| 4.9839  | 1550 | 0.0001        | -               |
| 5.1447  | 1600 | 0.0001        | -               |
| 5.3055  | 1650 | 0.0001        | -               |
| 5.4662  | 1700 | 0.0002        | -               |
| 5.6270  | 1750 | 0.0           | -               |
| 5.7878  | 1800 | 0.0           | -               |
| 5.9486  | 1850 | 0.0           | -               |
| 6.1093  | 1900 | 0.0001        | -               |
| 6.2701  | 1950 | 0.0           | -               |
| 6.4309  | 2000 | 0.0           | -               |
| 6.5916  | 2050 | 0.0           | -               |
| 6.7524  | 2100 | 0.0           | -               |
| 6.9132  | 2150 | 0.0002        | -               |
| 7.0740  | 2200 | 0.0002        | -               |
| 7.2347  | 2250 | 0.0           | -               |
| 7.3955  | 2300 | 0.0           | -               |
| 7.5563  | 2350 | 0.0           | -               |
| 7.7170  | 2400 | 0.0           | -               |
| 7.8778  | 2450 | 0.0           | -               |
| 8.0386  | 2500 | 0.0           | -               |
| 8.1994  | 2550 | 0.0           | -               |
| 8.3601  | 2600 | 0.0           | -               |
| 8.5209  | 2650 | 0.0           | -               |
| 8.6817  | 2700 | 0.0           | -               |
| 8.8424  | 2750 | 0.0           | -               |
| 9.0032  | 2800 | 0.0           | -               |
| 9.1640  | 2850 | 0.0           | -               |
| 9.3248  | 2900 | 0.0           | -               |
| 9.4855  | 2950 | 0.0           | -               |
| 9.6463  | 3000 | 0.0           | -               |
| 9.8071  | 3050 | 0.0           | -               |
| 9.9678  | 3100 | 0.0           | -               |
| 10.1286 | 3150 | 0.0           | -               |
| 10.2894 | 3200 | 0.0           | -               |
| 10.4502 | 3250 | 0.0           | -               |
| 10.6109 | 3300 | 0.0           | -               |
| 10.7717 | 3350 | 0.0           | -               |
| 10.9325 | 3400 | 0.0           | -               |
| 11.0932 | 3450 | 0.0           | -               |
| 11.2540 | 3500 | 0.0           | -               |
| 11.4148 | 3550 | 0.0           | -               |
| 11.5756 | 3600 | 0.0           | -               |
| 11.7363 | 3650 | 0.0           | -               |
| 11.8971 | 3700 | 0.0           | -               |
| 12.0579 | 3750 | 0.0004        | -               |
| 12.2186 | 3800 | 0.0           | -               |
| 12.3794 | 3850 | 0.0001        | -               |
| 12.5402 | 3900 | 0.0001        | -               |
| 12.7010 | 3950 | 0.0           | -               |
| 12.8617 | 4000 | 0.0001        | -               |
| 13.0225 | 4050 | 0.0002        | -               |
| 13.1833 | 4100 | 0.0009        | -               |
| 13.3441 | 4150 | 0.0037        | -               |
| 13.5048 | 4200 | 0.0025        | -               |
| 13.6656 | 4250 | 0.0009        | -               |
| 13.8264 | 4300 | 0.0002        | -               |
| 13.9871 | 4350 | 0.0002        | -               |
| 14.1479 | 4400 | 0.0           | -               |
| 14.3087 | 4450 | 0.0002        | -               |
| 14.4695 | 4500 | 0.0001        | -               |
| 14.6302 | 4550 | 0.0004        | -               |
| 14.7910 | 4600 | 0.0008        | -               |
| 14.9518 | 4650 | 0.0           | -               |
| 15.1125 | 4700 | 0.0           | -               |
| 15.2733 | 4750 | 0.0001        | -               |
| 15.4341 | 4800 | 0.0           | -               |
| 15.5949 | 4850 | 0.0           | -               |
| 15.7556 | 4900 | 0.0002        | -               |
| 15.9164 | 4950 | 0.0           | -               |
| 16.0772 | 5000 | 0.0           | -               |
| 16.2379 | 5050 | 0.0001        | -               |
| 16.3987 | 5100 | 0.0           | -               |
| 16.5595 | 5150 | 0.0           | -               |
| 16.7203 | 5200 | 0.0           | -               |
| 16.8810 | 5250 | 0.0           | -               |
| 17.0418 | 5300 | 0.0           | -               |
| 17.2026 | 5350 | 0.0           | -               |
| 17.3633 | 5400 | 0.0           | -               |
| 17.5241 | 5450 | 0.0           | -               |
| 17.6849 | 5500 | 0.0           | -               |
| 17.8457 | 5550 | 0.0           | -               |
| 18.0064 | 5600 | 0.0           | -               |
| 18.1672 | 5650 | 0.0           | -               |
| 18.3280 | 5700 | 0.0           | -               |
| 18.4887 | 5750 | 0.0           | -               |
| 18.6495 | 5800 | 0.0           | -               |
| 18.8103 | 5850 | 0.0           | -               |
| 18.9711 | 5900 | 0.0           | -               |
| 19.1318 | 5950 | 0.0           | -               |
| 19.2926 | 6000 | 0.0           | -               |
| 19.4534 | 6050 | 0.0           | -               |
| 19.6141 | 6100 | 0.0           | -               |
| 19.7749 | 6150 | 0.0           | -               |
| 19.9357 | 6200 | 0.0           | -               |
| 20.0965 | 6250 | 0.0           | -               |
| 20.2572 | 6300 | 0.0           | -               |
| 20.4180 | 6350 | 0.0           | -               |
| 20.5788 | 6400 | 0.0           | -               |
| 20.7395 | 6450 | 0.0           | -               |
| 20.9003 | 6500 | 0.0           | -               |
| 21.0611 | 6550 | 0.0           | -               |
| 21.2219 | 6600 | 0.0           | -               |
| 21.3826 | 6650 | 0.0           | -               |
| 21.5434 | 6700 | 0.0           | -               |
| 21.7042 | 6750 | 0.0           | -               |
| 21.8650 | 6800 | 0.0           | -               |
| 22.0257 | 6850 | 0.0           | -               |
| 22.1865 | 6900 | 0.0           | -               |
| 22.3473 | 6950 | 0.0           | -               |
| 22.5080 | 7000 | 0.0           | -               |
| 22.6688 | 7050 | 0.0           | -               |
| 22.8296 | 7100 | 0.0           | -               |
| 22.9904 | 7150 | 0.0           | -               |
| 23.1511 | 7200 | 0.0           | -               |
| 23.3119 | 7250 | 0.0           | -               |
| 23.4727 | 7300 | 0.0           | -               |
| 23.6334 | 7350 | 0.0           | -               |
| 23.7942 | 7400 | 0.0           | -               |
| 23.9550 | 7450 | 0.0           | -               |
| 24.1158 | 7500 | 0.0           | -               |
| 24.2765 | 7550 | 0.0           | -               |
| 24.4373 | 7600 | 0.0           | -               |
| 24.5981 | 7650 | 0.0           | -               |
| 24.7588 | 7700 | 0.0           | -               |
| 24.9196 | 7750 | 0.0           | -               |
| 25.0804 | 7800 | 0.0           | -               |
| 25.2412 | 7850 | 0.0           | -               |
| 25.4019 | 7900 | 0.0           | -               |
| 25.5627 | 7950 | 0.0           | -               |
| 25.7235 | 8000 | 0.0           | -               |
| 25.8842 | 8050 | 0.0           | -               |
| 26.0450 | 8100 | 0.0           | -               |
| 26.2058 | 8150 | 0.0           | -               |
| 26.3666 | 8200 | 0.0           | -               |
| 26.5273 | 8250 | 0.0           | -               |
| 26.6881 | 8300 | 0.0           | -               |
| 26.8489 | 8350 | 0.0           | -               |
| 27.0096 | 8400 | 0.0           | -               |
| 27.1704 | 8450 | 0.0           | -               |
| 27.3312 | 8500 | 0.0           | -               |
| 27.4920 | 8550 | 0.0           | -               |
| 27.6527 | 8600 | 0.0           | -               |
| 27.8135 | 8650 | 0.0           | -               |
| 27.9743 | 8700 | 0.0           | -               |
| 28.1350 | 8750 | 0.0           | -               |
| 28.2958 | 8800 | 0.0           | -               |
| 28.4566 | 8850 | 0.0           | -               |
| 28.6174 | 8900 | 0.0           | -               |
| 28.7781 | 8950 | 0.0           | -               |
| 28.9389 | 9000 | 0.0           | -               |
| 29.0997 | 9050 | 0.0           | -               |
| 29.2605 | 9100 | 0.0           | -               |
| 29.4212 | 9150 | 0.0           | -               |
| 29.5820 | 9200 | 0.0           | -               |
| 29.7428 | 9250 | 0.0           | -               |
| 29.9035 | 9300 | 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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