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: 한양 충전식 온수 찜질기 온열 BEST 벨트형 전기 찜질팩 배 허리 목 어깨 복대 핫팩 벨트형_보라색 구름모양 주식회사 원삼메디
- text: '충전식 온수 찜질기 온열 전기 찜질팩 IVB-D1000 핑크 '
- text: 메이스 보온 물주머니 찜질팩 온열 허리 배 복부 온수 온찜질 핫팩 보온주머니 2L 보온물주머니_1L 브라운 메이스코리아
- text: 슈슈엔젤 연두 팥 찜질팩 핫팩 주머니 부모님 선물 1_선택7 꽃팥찜질팩 슈슈엔젤123
- text: 온감테라피 온열 목 마스크 5매 x 5개 / 컨디션 케어 1.온감테라피 온열 목 마스크 5매입 x 5개 라이온코리아 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: 0.9710382513661202
      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:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
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### 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                                                                                                                                                                                         |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1.0   | <ul><li>'파쉬 독일 보온 물주머니 노커버 기본형 커버 체크 핑크네이비 주식회사 하이유로'</li><li>'파쉬 독일 보온 물주머니 노커버 기본형 3.노커버 기본형 레드 주식회사 하이유로'</li><li>'꼼띠아 국산 프리미엄 온열 황토 순면 냉 온 어깨 찜질기 찜질팩 목 등 찜질 쿨매트 허리찜질기(그레이) BH스토어'</li></ul> |
| 0.0   | <ul><li>'한양 온수찜질기 밍크 파우치 회색_SET 밍크파우치+복대 한양의료기'</li><li>'한양 온수찜질기 밍크 파우치 블랙_밍크 발찜질기 한양의료기'</li><li>'게르마늄 전기찜질기 뜸질기 찜질기 찜질팩 전기메트 허리 배 무릎 찜질 MinSellAmount 스마일배송'</li></ul>                        |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.9710 |

## 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_lh5")
# Run inference
preds = model("충전식 온수 찜질기 온열 전기 찜질팩 IVB-D1000 핑크 ")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 4   | 10.73  | 20  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 50                    |
| 1.0   | 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0625 | 1    | 0.3748        | -               |
| 3.125  | 50   | 0.0002        | -               |
| 6.25   | 100  | 0.0           | -               |
| 9.375  | 150  | 0.0           | -               |
| 12.5   | 200  | 0.0           | -               |
| 15.625 | 250  | 0.0           | -               |
| 18.75  | 300  | 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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