---
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: 코어슬리머 전용 리필패드 6P 2개  롯데아이몰
- text: 발락 손목 마사지기 안마기 간편한 EMS 반영구적 통증 팔목 마사지 발락 손목 마사지기 세트 (주)엘가니
- text: '[바이오프로테크]프로텐스 핀타입 대형 저주파패드 2조(RG01)  '
- text: 성게 탱탱볼 노인복지센터 안마볼 촉각볼 선물 몸신 물리치료 어르신 탱볼_11.탱볼(농구) 워커스
- text: '[약손드림] 저주파 EMS 어깨 마사지기 미세전류 휴대용 안마기 부모님선물 효도선물 어깨보호대 M(95~100호) 금양리테일 주식회사'
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: 0.894511760513186
      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:** 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>'예림전자 적외선조사기 전체화이트 필립스 250W 램프 적외선 치료기 아닌 국산 의료기기 01 전체화이트 e청춘'</li><li>'비타그램 필립스 적외선 램프 피부방사기 WGT-8888S  VitaGRAM'</li><li>'원적외선 온열 치료기 한의원 어깨 경추 램프 마사지 MinSellAmount 차류소'</li></ul>                                 |
| 2.0   | <ul><li>'HWATO 고급형 부항기 14컵  라이프샵'</li><li>'손 사혈부항용 따주기 자 통사혈기 광명사 침 구비 측정 습식 손따주는 체했을때 혈당기 자동 간편  알리몽드투'</li><li>'한솔부항기 신형 소독가능 부항컵 10개 1박스 (사이즈선택1-5호) 한솔부항2호컵 수의료기'</li></ul>                                                |
| 5.0   | <ul><li>'오므론 저주파 롱 라이프 패드 2p HV-LLPAD-G... 1개 HV-LLPAD-GY × 2개 스위에'</li><li>'코어슬리머 전용 리필패드 6P 3개 [0001]기본상품 CJONSTYLE'</li><li>'클럭 미니 마사지기 리필패드 큰패드 2박스 총6P /DY_MC  멸치쇼핑'</li></ul>                                            |
| 0.0   | <ul><li>'닥터체크 슬림 X형 테이핑 무릎보호대(좌우겸용 1P) M-중형(630475)  트래이드 씨스템(TRADE SYSTEM)'</li><li>'닥터체크 슬림 X형 테이핑 종아리압박보호대(좌우겸용 1P) M-중형(630499)  태빛ID'</li><li>'국산 의료용 허리보호대 편안하고 부드러운 허리복대 선택01- 001s 허리보호대_XXXL(40~43인치) 대한건강'</li></ul> |
| 4.0   | <ul><li>'스트라텍 의료용 전침기 4채널 STN-220 저주파자극기 침전기자극기 자석형 (주)오픈메디칼'</li><li>'디웰 저주파 마사지기 버튼형 LB-1803 미니마사지기 휴대용 무선 안마기 일반구매_06.버튼형2박스+대형패드 8매+흡착컵8개 주식회사 청훈'</li><li>'극동저주파 PRO1000 wave GOLD  헬스푸드메디칼'</li></ul>                    |
| 1.0   | <ul><li>'조은팜 초음파젤 의료용젤 투명5L 1통 무료전달 조은초음파젤5L블루 세븐메디컬'</li><li>'이도팜 소노젤리 투명 블루 5L +250ml 공병 소노겔 초음파젤리 ECG [0001]블루 5L CJONSTYLE'</li><li>'세니피아 에코소닉 초음파젤 투명 250mL 12개x4통 1박스 소노젤리 피부과 산부인과용  세븐메디컬'</li></ul>                   |
| 3.0   | <ul><li>'클럭 미니 마사지기SE  YGGlobal'</li><li>'온열/공기압/원적외선/저주파 4중케어 무릎마사지기[공기압 온열 원적외선 진동기능]안마기 05.클레버 마사지건 SR825 수련닷컴'</li><li>'휴테크 하체 근육 강화 EMS 마사지기 식스패드 풋핏2 HT-W03A  '</li></ul>                                                |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.8945 |

## 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_lh9")
# Run inference
preds = model("코어슬리머 전용 리필패드 6P 2개  롯데아이몰")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 9.78   | 21  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 50                    |
| 1.0   | 50                    |
| 2.0   | 50                    |
| 3.0   | 50                    |
| 4.0   | 50                    |
| 5.0   | 50                    |
| 6.0   | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0182  | 1    | 0.4065        | -               |
| 0.9091  | 50   | 0.2829        | -               |
| 1.8182  | 100  | 0.0954        | -               |
| 2.7273  | 150  | 0.0196        | -               |
| 3.6364  | 200  | 0.0057        | -               |
| 4.5455  | 250  | 0.0069        | -               |
| 5.4545  | 300  | 0.0024        | -               |
| 6.3636  | 350  | 0.0003        | -               |
| 7.2727  | 400  | 0.0002        | -               |
| 8.1818  | 450  | 0.0001        | -               |
| 9.0909  | 500  | 0.0001        | -               |
| 10.0    | 550  | 0.0001        | -               |
| 10.9091 | 600  | 0.0001        | -               |
| 11.8182 | 650  | 0.0001        | -               |
| 12.7273 | 700  | 0.0001        | -               |
| 13.6364 | 750  | 0.0001        | -               |
| 14.5455 | 800  | 0.0001        | -               |
| 15.4545 | 850  | 0.0001        | -               |
| 16.3636 | 900  | 0.0001        | -               |
| 17.2727 | 950  | 0.0001        | -               |
| 18.1818 | 1000 | 0.0001        | -               |
| 19.0909 | 1050 | 0.0           | -               |
| 20.0    | 1100 | 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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