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: 여성 가방 숄더백 미니 크로스백 퀼팅백 체인백 토트백 미니백 여자 핸드백 구름백 클러치백 직장인 백팩 프리아_카멜 더블유팝
- text: 국내 잔스포츠 백팩 슈퍼브레이크 4QUT 블랙 학생 여성 가벼운 가방 캠핑 여행 당일  가원
- text: 국내생산 코튼 양줄면주머니 미니&에코 주머니 7종 학원 학교 만들기수업 양줄주머니_14cmX28cm(J14) 명성패키지
- text: 웨빙 플라워 스트랩 레디백 길이조절 가방끈 어깨끈 리폼 3-플라워가방끈-흰색 이백프로
- text: 엔비조네/가방끈/가방끈리폼/가죽끈/크로스끈/숄더끈/스트랩 AOR오링25mm_블랙오플_폭11mm *35cm 니켈 엔비조네
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: 0.7867699642431466
      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:** 10 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                                                                                                                                                                                                                             |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 3.0   | <ul><li>'[현대백화점][루이까또즈] MOONMOON(문문) 여성호보백 HR3SO02BL  (주)현대백화점'</li><li>'소프트레더 파스텔 보부상 빅숄더백 휘뚜루마뚜루가방 토드백 블랙_one size 아이디어코리아 주식회사'</li><li>'DRAGON DIFFUSION 드래곤디퓨전 폼폼 더블 점프백 여성 버킷백 8838 드래곤백 다크브라운 (DARK BROWN) 시계1위워치짱'</li></ul> |
| 7.0   | <ul><li>'디어 4colors_H70301010 (W)퍼플와인 '</li><li>'[마이클코어스][정상가 1080000원] 에밀리아 라지 레더 사첼 35H0GU5S7T2171  신세계몰'</li><li>'칼린 소프트M 10colors _H71307020 (Y)라임네온_one size (주)칼린홍대점'</li></ul>                                                |
| 1.0   | <ul><li>'마젤란9901 메신저백 크로스백 학생 여행용 가방 백팩 1_MA-9901-BlackPurple(+LK) 더블유팝'</li><li>'마젤란9901 메신저백 크로스백 학생 여행용 가방 백팩 1_MA-9901-D.Gray(+LK) 더블유팝'</li><li>'마젤란9901 메신저백 크로스백 학생 여행용 가방 백팩 1_MA-9901-Black(+LK) 더블유팝'</li></ul>            |
| 9.0   | <ul><li>'룰루레몬 에브리웨어 벨트 백 Fleece WHTO/GOLD White Opal/Gold - O/S 오늘의원픽'</li><li>'[리본즈] LEMAIRE 남성 숄더백 37408558 블랙_ONE SIZE/단일상품 마리오아울렛몰'</li><li>'[코치][공식] 홀 벨트 백 CU103 WYE [00001] 없음 현대백화점'</li></ul>                               |
| 0.0   | <ul><li>'가죽가방끈 천연소가죽 가죽 스트랩 32Color 블랙12mm페이던트골드 대성메디칼'</li><li>'[최초가 228,000원][잘모이] 밍크 듀에 퍼 스트랩 LTZ-5205 168688 와인스카이블루 주식회사 미르에셋'</li><li>'[조이그라이슨](강남점) 첼시 스트랩 LW4SX6880_55 GOLD 신세계백화점'</li></ul>                                |
| 5.0   | <ul><li>'[소마치] 트래블 여권 지갑 파우치 핸드폰 미니 크로스백 카키_체인105cm(키160전후) 주식회사 소마치'</li><li>'비비안웨스트우드 코튼 숄더백 EDGWARE (3컬러) chacoal(당일발송) KHY INTERNATIONAL'</li><li>'남여 공용 미니 메신저백 귀여운 크로스백 학생 미니백 여행 보조 가방 여행용 보조백 아이보리 구공구코리아'</li></ul>         |
| 2.0   | <ul><li>'메종미네드 MAISON MINED TWO POCKET BACKPACK S OC오피스'</li><li>'백팩01K1280ZSK외1종 블랙 롯데백화점1관'</li><li>'ANC CLASSIC BACKPACK_BLACK BLACK 주식회사 데일리컴퍼니'</li></ul>                                                                       |
| 4.0   | <ul><li>'[스타벅스]텀블러 가방 컵홀더 데일리 캔버스 에코백 지퍼형_베이지 씨에스 인더스트리'</li><li>'마리떼 FRANCOIS GIRBAUD CLASSIC LOGO ECO BAG natural OS 다함'</li><li>'마크 곤잘레스 Print Eco Bag - 블랙 568032 BLACK_FREE 라임e커머스'</li></ul>                                   |
| 8.0   | <ul><li>'국내생산 코튼 양줄면주머니 미니&에코 주머니 7종 학원 학교 만들기수업 양줄주머니_20cmX25cm(J20) 명성패키지'</li><li>'조리개 타입 반투명 파우치 보관 신발주머니 주머니 끈주머니 끈파우치 신주머니 여행용 중형(25X35) 정바른 길정'</li><li>'국내생산 코튼 화이트&블랙주머니 학원 학교 주머니만들기 W15_화이트 명성패키지'</li></ul>              |
| 6.0   | <ul><li>'메종 마르지엘라 타비 스니커즈 S37WS0578 P4291 T1003 EU41(260-265) 보광컴퍼니'</li><li>'[롯데백화점]루이까또즈 클러치백 MO2DL03MDABL  롯데백화점_'</li><li>'깔끔한 여성용 데일리 핸드 스트랩 클러치 가방 남자클러치백  로우마켓'</li></ul>                                                     |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.7868 |

## 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_ac9")
# Run inference
preds = model("웨빙 플라워 스트랩 레디백 길이조절 가방끈 어깨끈 리폼 3-플라워가방끈-흰색 이백프로")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 9.6146 | 30  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 50                    |
| 1.0   | 17                    |
| 2.0   | 50                    |
| 3.0   | 50                    |
| 4.0   | 50                    |
| 5.0   | 50                    |
| 6.0   | 50                    |
| 7.0   | 50                    |
| 8.0   | 50                    |
| 9.0   | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0137  | 1    | 0.4278        | -               |
| 0.6849  | 50   | 0.3052        | -               |
| 1.3699  | 100  | 0.1524        | -               |
| 2.0548  | 150  | 0.0583        | -               |
| 2.7397  | 200  | 0.0292        | -               |
| 3.4247  | 250  | 0.0197        | -               |
| 4.1096  | 300  | 0.0061        | -               |
| 4.7945  | 350  | 0.0022        | -               |
| 5.4795  | 400  | 0.0033        | -               |
| 6.1644  | 450  | 0.0003        | -               |
| 6.8493  | 500  | 0.0002        | -               |
| 7.5342  | 550  | 0.0001        | -               |
| 8.2192  | 600  | 0.0001        | -               |
| 8.9041  | 650  | 0.0001        | -               |
| 9.5890  | 700  | 0.0001        | -               |
| 10.2740 | 750  | 0.0001        | -               |
| 10.9589 | 800  | 0.0001        | -               |
| 11.6438 | 850  | 0.0001        | -               |
| 12.3288 | 900  | 0.0001        | -               |
| 13.0137 | 950  | 0.0001        | -               |
| 13.6986 | 1000 | 0.0001        | -               |
| 14.3836 | 1050 | 0.0001        | -               |
| 15.0685 | 1100 | 0.0001        | -               |
| 15.7534 | 1150 | 0.0001        | -               |
| 16.4384 | 1200 | 0.0001        | -               |
| 17.1233 | 1250 | 0.0           | -               |
| 17.8082 | 1300 | 0.0001        | -               |
| 18.4932 | 1350 | 0.0001        | -               |
| 19.1781 | 1400 | 0.0001        | -               |
| 19.8630 | 1450 | 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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