---
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: '[당일출고/백화점정품] 나스 래디언트 크리미 컨실러 6ml / 바닐라 바닐라 에스엠(SM)월드'
- text: '[갤러리아] [수분 피팅 프라이머] 프로텍션 SPF 50 PA+++(한화갤러리아㈜ 광교점) 프로텍션 SPF 50 PA+++ 한화갤러리아(주)'
- text: '[빌리프] [24MS]시카 밤 쿠션 핑크 베이지 기본 주식회사 인터파크커머스'
- text: (백화) 오휘 24RN 얼티밋 커버 메쉬 쿠션 1호 383007 옵션없음 펀펀몰
- text: 나스 래디언스 프라이머 30ml(SPF35) 옵션없음 블루밍컴퍼니
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: 0.7155172413793104
      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                                                                                                                                                                                           |
|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0.0   | <ul><li>'콜라겐 비비크림 50g 23호 옵션없음 심완태'</li><li>'본체청정 물광 커버력 좋은 재생 톤업 bb 비비 크림 연 퍼펙트 매직 50ml 옵션없음 에테르'</li><li>'빈토르테 미네랄 CC크림 자외선차단 SPF50+ 30g 옵션없음 토스토'</li></ul>                                     |
| 3.0   | <ul><li>'바비브라운 코렉터 1.4g 피치 비스크 호이컴퍼니'</li><li>'더샘 커버 퍼펙션 트리플 팟 컨실러 5colors 04 톤업 베이지 주식회사 더샘인터내셔날'</li><li>'티핏 tfit 커버 업 프로 컨실러 15G 03 쿨 티핏클래스 주식회사'</li></ul>                                     |
| 1.0   | <ul><li>'누즈 케어 톤업 30ml(SPF50+) 옵션없음 달토끼네멋진마켓'</li><li>'MAC 맥 스트롭 크림 50ml 피치라이트 호이컴퍼니'</li><li>'더후 공진향 미 럭셔리 선베이스 45ml33881531 옵션없음 씨플랩몰'</li></ul>                                                 |
| 5.0   | <ul><li>'에이지투웨니스 벨벳 래스팅 팩트 14g + 14g(리필, SPF50+) 미디움베이지 위브로5'</li><li>'메리쏘드 릴커버 멜팅팩트 본품 11g + 리필 11g +퍼프2개 내추럴베이지(본품+리필)+퍼프2개 주식회사 벨라솔레'</li><li>'퓌 쿠션 스웨이드 15g(SPF50+) 누드스웨이드(03) 강원상회'</li></ul> |
| 4.0   | <ul><li>'쥬리아 루나리스 실키 핏 스킨카바 23호리필내장 옵션없음 에테르노'</li><li>'Almay 프레스드 파우더 올 세트 노 샤인, 마이 베스트 라이트, [100] 0.20 oz 옵션없음 케이피스토어'</li><li>'철벽보습커버 21호 리필내장 쥬얼성분배합 투웨이케익 옵션없음 후니후니003'</li></ul>             |
| 6.0   | <ul><li>'VDL 루미레이어 프라이머 30ml 옵션없음 페퍼파우더'</li><li>'어바웃톤 블러 래스팅 스틱 프라이머 10g AT.블러 래스팅 스틱 프라이머 (주)삐아'</li><li>'로라 메르시에 퓨어 캔버스 프라이머 25ml - 트래블 사이즈 하이드레이팅 고온누리'</li></ul>                              |
| 2.0   | <ul><li>'후 공진향 미 럭셔리 비비 스페셜 세트 267578 옵션없음 펀펀마켓'</li><li>'케이트 리얼 커버 리퀴드 파운데이션 세미 매트 + 스틱컨실러 A 세트 케이트'</li><li>'커버력높은 쿠션팩트 승무원팩트 본품+리필 or 광채CC크림 2종세트 SPF 50+ 뷰디아니'</li></ul>                       |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.7155   |

## 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_bt4_test")
# Run inference
preds = model("나스 래디언스 프라이머 30ml(SPF35) 옵션없음 블루밍컴퍼니")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 5   | 9.7872 | 19  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 19                    |
| 1.0   | 21                    |
| 2.0   | 10                    |
| 3.0   | 19                    |
| 4.0   | 28                    |
| 5.0   | 23                    |
| 6.0   | 21                    |

### 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0588  | 1    | 0.499         | -               |
| 2.9412  | 50   | 0.3295        | -               |
| 5.8824  | 100  | 0.0469        | -               |
| 8.8235  | 150  | 0.0217        | -               |
| 11.7647 | 200  | 0.0013        | -               |
| 14.7059 | 250  | 0.0001        | -               |
| 17.6471 | 300  | 0.0001        | -               |
| 20.5882 | 350  | 0.0           | -               |
| 23.5294 | 400  | 0.0           | -               |
| 26.4706 | 450  | 0.0           | -               |
| 29.4118 | 500  | 0.0           | -               |
| 32.3529 | 550  | 0.0           | -               |
| 35.2941 | 600  | 0.0           | -               |
| 38.2353 | 650  | 0.0           | -               |
| 41.1765 | 700  | 0.0           | -               |
| 44.1176 | 750  | 0.0           | -               |
| 47.0588 | 800  | 0.0           | -               |
| 50.0    | 850  | 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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