---
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: 선인장 소프트렌즈 렌즈세척기 수동 셀프 세척 필수선택_핑크 은총에벤에셀
- text: '[멕리듬]메구리즘/멕리듬 아이마스크 수면안대 12입 5.잘 익은 유자향 12P 롯데아이몰'
- text: 교체용 케이스 소프트 집게 거울 콘텍트 세트 블루 슈가랜드
- text: 보아르 아이워시 초음파 안경 렌즈세척기 눈에보이지 않는 각종 세균 99.7% 완벽세척 화이트 U0001 오아 주식회사
- text: 안대 야옹이 찜질 2종 눈찜질 여행 수면 캐릭터 블랙 엠포엘
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: 0.9615384615384616
      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:** 4 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>'굿나잇 온열안대 수면안대 눈찜질 눈찜질기 눈찜질팩 MinSellAmount 오아월드'</li><li>'[대구백화점] [누리아이]안구건조증 치료의료기기 누리아이 5800 (위생용시트지 1박스 ) 누리아이 5800 대구백화점'</li><li>'동국제약 굿잠 스팀안대 3박스 수면 온열안대 (무향/카모마일향 선택) 1_무향 3박스_AA 동국제약_본사직영'</li></ul> |
| 0.0   | <ul><li>'렌즈집게 렌즈 넣는 집게 끼는 도구 흡착봉 소프트 렌즈집게(핑크) 썬더딜'</li><li>'메루루 원데이 소프트렌즈 집게 착용 분리 기구 1세트 MinSellAmount 체리팝스'</li><li>'소프트 통 케이스 빼는도구 접시 용품 흡착봉 뽁뽁이 보관통 하드 렌즈통(블루) 기쁘다희샵'</li></ul>                                    |
| 2.0   | <ul><li>'초음파 변환장치 진동기 식기 세척기 진동판 생성기 초음파발생기 변환기 D. 20-40K1800W (비고 주파수) 메타몰'</li><li>'새한 초음파세정기 SH-1050 / 28kHz / 1.2L / 신제품  주식회사 전자코리아'</li><li>'새한 디지털 초음파 세척기 세정기 SH-1050D 안경 렌즈 귀금속 세척기  서진하이텍'</li></ul>         |
| 1.0   | <ul><li>'휴먼바이오 식염수 중외제약 셀라인 식염수 370ml 20개, 드림 하드 렌즈용 생리 식염수 가이아코리아 휴먼바이오 식염수 500ml 20개 가이아코리아(Gaia Korea)'</li><li>'리뉴 센서티브 355ml  씨채널안경체인태백점'</li><li>'바슈롬 바이오트루 300ml  쏜 상점'</li></ul>                               |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.9615 |

## 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_lh6")
# Run inference
preds = model("교체용 케이스 소프트 집게 거울 콘텍트 세트 블루 슈가랜드")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 9.705  | 19  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 50                    |
| 1.0   | 50                    |
| 2.0   | 50                    |
| 3.0   | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0312  | 1    | 0.4002        | -               |
| 1.5625  | 50   | 0.064         | -               |
| 3.125   | 100  | 0.0021        | -               |
| 4.6875  | 150  | 0.0004        | -               |
| 6.25    | 200  | 0.0001        | -               |
| 7.8125  | 250  | 0.0001        | -               |
| 9.375   | 300  | 0.0           | -               |
| 10.9375 | 350  | 0.0           | -               |
| 12.5    | 400  | 0.0           | -               |
| 14.0625 | 450  | 0.0           | -               |
| 15.625  | 500  | 0.0           | -               |
| 17.1875 | 550  | 0.0           | -               |
| 18.75   | 600  | 0.0           | -               |

### 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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