README.md

---
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: 진주 선 마린 스프레이 180ml 펄 제이엠솔루션 청광 옵션없음 사구있오
- text: 핑크 레오파드 호피무늬 태닝비키니 바디프로필 몸매 - 표범 표범_M 세일러7
- text: 제이엠솔루션 180ml 스프레이 선 진주 마린 펄 청광 옵션없음 뿔샵
- text: 제주온 큐테라 풋귤 알로에베라 수딩젤 200ml × 1개 구대연구소
- text: BALIBODY SPF 6 카카오 태닝 오일 100ml x 5개 옵션없음 시연마켓
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: 0.7882599580712788
      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:** 7 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                                                                                                                                                                                                                                  |
|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 6.0   | <ul><li>'디자이너스킨 올 액세스 태닝로션 여름 크림 옵션없음 라이브프롬잇'</li><li>'코스노리 올웨이즈 핏 바 비키니 코드 바디젤 263003 J 옵션없음 제이피샵온(JPshopon)'</li><li>'Bronzer Tanning Lotion by 디자이너스킨 400ml 옵션없음 라이브프롬잇'</li></ul>                                                     |
| 5.0   | <ul><li>'썬범 프리미엄 하이드레이팅 애프터 썬 젤 쿨다운 237ml - 썬 젤 쿨다운 237ml x 2개_없음 나대몰'</li><li>'Allurials 알로에 베라 젤 354ml 옵션없음 해외쇼핑 잘왔네'</li><li>'웰더마 지플러스 쿨링 에센스 알로에베라 수딩젤 120g × 3개 120g × 3개 하이블리스'</li></ul>                                           |
| 1.0   | <ul><li>'SVR 선시큐어 울트라 라이트 인비저블 선 스프레이 SPF50 200ml 옵션없음 주식회사하늘'</li><li>'선스프레이 청광 진주 마린 제이엠솔루션 선 스프레이 펄 옵션없음 유토피아'</li><li>'청광 선 180ml 스프레이 제이엠솔루션 마린 진주 펄 옵션없음 포뿔샵'</li></ul>                                                             |
| 0.0   | <ul><li>'큐어 쿨링 선스틱 23g 2개 옵션없음 씨유니'</li><li>'라운드랩 자작나무 수분 선스틱 19g SPF 50+ 자작 수분 선스틱 19g 원스원컴퍼니'</li><li>'AHC 내추럴 퍼펙션 더블 쉴드 선스틱 (파랑)14g 골프 등산 워터프루프 썬크림 옵션없음 위얼드(WEALD)'</li></ul>                                                         |
| 4.0   | <ul><li>'엘로엘 2024 시즌8 팡팡 빅선쿠션 S8 스마일썬쿠션 본품 25g 옵션없음 더블아이'</li><li>'엘로엘 팡팡 빅 선쿠션 시즌 8 본품25g + 리필25g 옵션없음 미소샵'</li><li>'BRTC 마일드 선쿠션 본품+리필 기획 (디즈니) 옵션없음 지구상사'</li></ul>                                                                    |
| 3.0   | <ul><li>'듀이셀 필터링크림 40ml (SPF50+) 옵션없음 오션컴퍼니'</li><li>'아쿠아선크림40ml+미니폼40ml / 24시간촉촉 비건 무기자차 SPF50+ PA++++ 옵션없음 에프엔지뷰티랩 주식회사'</li><li>'알리코제약 이나벨로 유기,무기자차 혼합자차 톤업 선크림 옵션없음 알리코제약(주)'</li></ul>                                               |
| 2.0   | <ul><li>'리쥬란 바이옴 힐러 선케어 2종 세트(바이옴 힐러 선 크림 50mL SPF50+ + 바이옴 힐러 선 밤 19g SPF50+) 리쥬란 힐러'</li><li>'라로제 클린 선스틱 SPF50 18.5g+수분스틱 15ml 세트 라로제 코스메틱'</li><li>'[로우퀘스트] 베리어 인핸싱 선크림(SPF50+PA++++) + 에키네시아 선스틱(SPF50+PA++++) 2종 세트 로우퀘스트'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.7883   |

## 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_bt7_test")
# Run inference
preds = model("제주온 큐테라 풋귤 알로에베라 수딩젤 200ml × 1개 구대연구소")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 4   | 10.1504 | 24  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 20                    |
| 1.0   | 10                    |
| 2.0   | 17                    |
| 3.0   | 28                    |
| 4.0   | 20                    |
| 5.0   | 15                    |
| 6.0   | 23                    |

### Training Hyperparameters
- batch_size: (512, 512)
- num_epochs: (50, 50)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 60
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0625 | 1    | 0.4924        | -               |
| 3.125  | 50   | 0.291         | -               |
| 6.25   | 100  | 0.0536        | -               |
| 9.375  | 150  | 0.0011        | -               |
| 12.5   | 200  | 0.0002        | -               |
| 15.625 | 250  | 0.0001        | -               |
| 18.75  | 300  | 0.0001        | -               |
| 21.875 | 350  | 0.0001        | -               |
| 25.0   | 400  | 0.0001        | -               |
| 28.125 | 450  | 0.0001        | -               |
| 31.25  | 500  | 0.0001        | -               |
| 34.375 | 550  | 0.0001        | -               |
| 37.5   | 600  | 0.0001        | -               |
| 40.625 | 650  | 0.0001        | -               |
| 43.75  | 700  | 0.0001        | -               |
| 46.875 | 750  | 0.0001        | -               |
| 50.0   | 800  | 0.0001        | -               |

### 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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