---
base_model: mini1013/master_domain
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 디스이즈 명화 디퓨저 리필 퓨어코튼 200ml (WB6AEE5) 본상품선택 기타/해당사항 없음
- text: 에르메스 떼르 데르메스EDT 50ml 옵션없음 주식회사 비엘컴퍼니
- text: '룸 디퓨저 코리앤더 200ml CL13965000200 투명_F 라부르켓(L:A BRUKET AB)/(주)신세계인터내셔날, 서울특별시
    강남구 도산대로 449, 소비자상담실: 1644-4490'
- text: '[향수] MAISON LOUIS MARIE 넘버13 누벨바그 퍼퓸오일 15ML509678 흰색_FREE(3Y6) 위원투고투'
- text: '(시시호시)훈옥당 다이고의 체리블로섬 인센스 멀티칼라(ML)_Free '
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: accuracy
      value: 0.9578313253012049
      name: Accuracy
---

# 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:** 3 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                                                                                                                                                                                          |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1.0   | <ul><li>'로얄워터 블랑쉬 코튼 비누향 베이비파우더 살냄새 수제 승무원 엑스트레 드 퍼퓸 30ml 24. 블루밍 (판매 1위) 주식회사 로얄워터'</li><li>'블루 드 샤넬 빠르펭 50ML 옵션없음 플로라 무역'</li><li>'딥티크 뗌포 오드 퍼퓸 75ml 옵션없음 대박컴퍼니'</li></ul>                      |
| 0.0   | <ul><li>'쿨티 - 스틸레 룸 디퓨저 - 린파 500ml/16.9oz 스트로베리넷 (홍콩)'</li><li>'소소모소 디퓨저리필 500ml_코튼브리즈 _salestrNo:2439_지점명:emartNE.O.001 (주)리빙탑스/해당사항 없음'</li><li>'디퓨저 섬유 리드스틱 화이트 50개입 디퓨저 섬유 옵션없음 '</li></ul>   |
| 2.0   | <ul><li>'인센스 스틱 홀더 접시형 그린 (WC9C73F) 본상품선택 기타/해당사항 없음'</li><li>'인센스홀더향 향꽂이 홀더 물방울 인테리어 인센스 (WD2F3FF) 본상품선택 기타/해당사항 없음'</li><li>'인센스 홀더 미니화병 황동 향 피우기 나그참파 꽂이 (WBC1E2F) 본상품선택 기타/해당사항 없음'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9578   |

## 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_bt10_test")
# Run inference
preds = model("에르메스 떼르 데르메스EDT 50ml 옵션없음 주식회사 비엘컴퍼니")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 5   | 9.4127 | 18  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 20                    |
| 1.0   | 23                    |
| 2.0   | 20                    |

### Training Hyperparameters
- batch_size: (512, 512)
- num_epochs: (50, 50)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 60
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-----:|:----:|:-------------:|:---------------:|
| 0.125 | 1    | 0.4915        | -               |
| 6.25  | 50   | 0.1556        | -               |
| 12.5  | 100  | 0.0           | -               |
| 18.75 | 150  | 0.0           | -               |
| 25.0  | 200  | 0.0           | -               |
| 31.25 | 250  | 0.0           | -               |
| 37.5  | 300  | 0.0           | -               |
| 43.75 | 350  | 0.0           | -               |
| 50.0  | 400  | 0.0           | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.3.1
- Transformers: 4.44.2
- PyTorch: 2.2.0a0+81ea7a4
- Datasets: 3.2.0
- Tokenizers: 0.19.1

## 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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