README.md

---
base_model: mini1013/master_domain
library_name: setfit
metrics:
- metric
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: '[현대백화점][비비안](RU1260) 40% 가격인하 순면 80수 기본 남성런닝 95 (주)현대백화점'
- text: 부드러운 터치감 남성 실켓가공 런닝 트렁크 팬티 세트 VMV4183VMP4183N/비너스 브라운_런닝105-팬티105 롯데쇼핑(주)
- text: '[리더스] 신축성 좋은 복부 코르셋 땀복 남자 바지 (15005144) 블랙_XL 신세계몰'
- text: 탑텐 탑텐 공용 플란넬 라운지웨어 세트 MSC4UI3001 rva-482878f BE_L(540) 라비아세개
- text: BYC 남성용 50수 순면 민소매 그랜드 런닝 2호 백색 1매 BYI6035 95 (주)대화언더웨어
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: metric
      value: 0.8497076023391813
      name: Metric
---

# 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:** 6 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                                                                                                                                                                                                                                                           |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 5.0   | <ul><li>'CK퍼포먼스 24 SUMMER 여름셋업 남여공용 4종 [0001]블랙 90(S) CJONSTYLE_LIVE'</li><li>'CALVIN KLEIN UNDERWEAR 여성 모던 코튼 T팬티_F3786D001 F3786D001 블랙_M 럭스펄스'</li><li>'[갭][갭] 옴므 트렁크 6종 택1 GPMTR1O30T 네이비/L(100) 패션플러스'</li></ul>                                              |
| 1.0   | <ul><li>'[와코루](신세계마산점)선염 모달 + 면 스판 스트라이프 조끼런닝 삼각 팬티 세트(WMV2378RWMP2378P) 95_100 주식회사 에스에스지닷컴'</li><li>'남자 속옷 등산 스포츠 SET 자전거 축구 스프츠 골프 백색_100 꼬북샵'</li><li>'싸이로컴팩 면모달 선염스트라이프 런닝RU1695T 네이비_100 신세계몰'</li></ul>                                                     |
| 3.0   | <ul><li>'CJ [리복] 스피드윅 기모 웜에어 상하의 2종 세트 남성 최신상 택일 옵션01.RBMYIEM01_00_100 (주)씨제이이엔엠'</li><li>'아르메데스 남성용 히트기모 발열내의 터틀넥 상의 AR-25 3매 블랙_M (주)아르메데스'</li><li>'[기능성 의류 BEST] 시원한 냉감 기능은 기본! 완벽한 자외선 차단! 기능성 티셔츠/조거팬츠/등산바지/아웃도어 의류 01.TM-MZS303_M_ZZGRY 테슬라_TSLA'</li></ul> |
| 0.0   | <ul><li>'남자 쿨 티셔츠 남성 냉감 나시티 기능성 반팔티 쿨링 EVE 화이트_100 에브리씽굿'</li><li>'비비안 모다아울렛 비비안 젠토프 텐셀솔리드 기본 반팔런닝 RU1239T 네이비_95 MODA아울렛'</li><li>'탑텐 TOPTEN 남성 쿨에어 크루넥 매쉬 탱크_MSD2UL1201 BK_100 가투투'</li></ul>                                                                    |
| 2.0   | <ul><li>'니플 나시 남자보정 속옷이너핏여유증커버남성뱃살가리개꼭지가슴압박복가리기티 남자보정나시 보급형/L/화이트 조니멀티샵'</li><li>'하라마키 배워머 더블 배워머 보온복대 남성용 HT-LunesDB-Charcoal-M BESTYOURS'</li><li>'고급 따뜻한 남자 밴딩 기모 레깅스 겨울 발열 내복 바지 보온 타이즈 블랙_2XL 사랑니'</li></ul>                                                  |
| 4.0   | <ul><li>'[오르시떼](센텀시티점)남성 D123 오니리크 반소매 상하 S 신세계백화점'</li><li>'(신세계마산점)오르시떼남성 D105 브데뜨 긴소매 상하 S 신세계백화점'</li><li>'JAJU 남 라이트 밍크 플리스 파자마 세트 블루 L 리치쇼핑'</li></ul>                                                                                                       |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.8497 |

## 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_ap0")
# Run inference
preds = model("[리더스] 신축성 좋은 복부 코르셋 땀복 남자 바지 (15005144) 블랙_XL 신세계몰")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 9.5967 | 24  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 50                    |
| 1.0   | 50                    |
| 2.0   | 50                    |
| 3.0   | 50                    |
| 4.0   | 50                    |
| 5.0   | 50                    |

### Training Hyperparameters
- batch_size: (512, 512)
- num_epochs: (20, 20)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 40
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch   | Step | Training Loss | Validation Loss |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0213  | 1    | 0.4362        | -               |
| 1.0638  | 50   | 0.3126        | -               |
| 2.1277  | 100  | 0.0687        | -               |
| 3.1915  | 150  | 0.0294        | -               |
| 4.2553  | 200  | 0.0006        | -               |
| 5.3191  | 250  | 0.0003        | -               |
| 6.3830  | 300  | 0.0002        | -               |
| 7.4468  | 350  | 0.0002        | -               |
| 8.5106  | 400  | 0.0001        | -               |
| 9.5745  | 450  | 0.0001        | -               |
| 10.6383 | 500  | 0.0001        | -               |
| 11.7021 | 550  | 0.0001        | -               |
| 12.7660 | 600  | 0.0001        | -               |
| 13.8298 | 650  | 0.0001        | -               |
| 14.8936 | 700  | 0.0001        | -               |
| 15.9574 | 750  | 0.0001        | -               |
| 17.0213 | 800  | 0.0001        | -               |
| 18.0851 | 850  | 0.0001        | -               |
| 19.1489 | 900  | 0.0001        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0.dev0
- Sentence Transformers: 3.1.1
- Transformers: 4.46.1
- PyTorch: 2.4.0+cu121
- Datasets: 2.20.0
- Tokenizers: 0.20.0

## 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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