README.md

---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 모던탑21 모던 클래식 800 3단 장식장 가구/인테리어>거실가구>장식장
- text: 스코나 마넌트 아쿠아텍스 패브릭 1인 리클라이너 소파 가구/인테리어>거실가구>소파>리클라이너소파
- text: 가구느낌 베스트책상 1000x400 접이식 간이 테이블 가구/인테리어>거실가구>테이블>접이식테이블
- text: 자코모 러버블 컴팩트 4인 스위브 소파 + 스툴 가구/인테리어>거실가구>소파>패브릭소파
- text: 미드센추리테이블 유리좌탁 거실소파테이블 1000 가구/인테리어>거실가구>테이블>거실테이블
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: mini1013/master_domain
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: 1.0
      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:** 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                                                                                                                                                                                                         |
|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1.0   | <ul><li>'에싸 라보엠Ⅲ 4인 오픈코너형 기능성 카시미라 패브릭 소파 가구/인테리어>거실가구>소파>패브릭소파'</li><li>'보루네오 플레타 3인용 천연소가죽 소파 가구/인테리어>거실가구>소파>가죽소파'</li><li>'동서가구 프라임 소나무원목 내추럴 황토 카우치 흙소파 DF638379 가구/인테리어>거실가구>소파>흙/돌소파'</li></ul>           |
| 2.0   | <ul><li>'체스 유리 진열장 가구/인테리어>거실가구>장식장'</li><li>'디자인벤처스 로맨틱 1800 유리장 가구/인테리어>거실가구>장식장'</li><li>'퍼니처스마트 로랜드 유리 장식장 가구/인테리어>거실가구>장식장'</li></ul>                                                                       |
| 0.0   | <ul><li>'나무뜰 켄트 서랍형 거실장 1200 티비다이 MRF013 가구/인테리어>거실가구>TV거실장'</li><li>'리바트 셀리나 1800 거실장 가구/인테리어>거실가구>TV거실장'</li><li>'슈퍼홈 리처 티비다이 낮은 거실장 2000 가구/인테리어>거실가구>TV거실장'</li></ul>                                        |
| 3.0   | <ul><li>'테이블 거실 커피 탁자 북유럽 좌식 인테리어 티 모던 카페 라운드 가구/인테리어>거실가구>테이블>거실테이블'</li><li>'미드센추리 테라조 협탁 사이드 테이블 거실 소파 장식장 선반형 가구/인테리어>거실가구>테이블>사이드테이블'</li><li>'원목좌식테이블 방석 세트 원형 차 홈 카페 거실 가구/인테리어>거실가구>테이블>거실테이블'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 1.0      |

## 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_fi1")
# Run inference
preds = model("모던탑21 모던 클래식 800 3단 장식장 가구/인테리어>거실가구>장식장")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 4   | 8.1714 | 18  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 70                    |
| 1.0   | 70                    |
| 2.0   | 70                    |
| 3.0   | 70                    |

### Training Hyperparameters
- batch_size: (256, 256)
- num_epochs: (30, 30)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 50
- 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0182  | 1    | 0.4862        | -               |
| 0.9091  | 50   | 0.4961        | -               |
| 1.8182  | 100  | 0.4367        | -               |
| 2.7273  | 150  | 0.0317        | -               |
| 3.6364  | 200  | 0.0           | -               |
| 4.5455  | 250  | 0.0           | -               |
| 5.4545  | 300  | 0.0           | -               |
| 6.3636  | 350  | 0.0           | -               |
| 7.2727  | 400  | 0.0           | -               |
| 8.1818  | 450  | 0.0           | -               |
| 9.0909  | 500  | 0.0           | -               |
| 10.0    | 550  | 0.0           | -               |
| 10.9091 | 600  | 0.0           | -               |
| 11.8182 | 650  | 0.0           | -               |
| 12.7273 | 700  | 0.0           | -               |
| 13.6364 | 750  | 0.0           | -               |
| 14.5455 | 800  | 0.0           | -               |
| 15.4545 | 850  | 0.0           | -               |
| 16.3636 | 900  | 0.0           | -               |
| 17.2727 | 950  | 0.0           | -               |
| 18.1818 | 1000 | 0.0           | -               |
| 19.0909 | 1050 | 0.0           | -               |
| 20.0    | 1100 | 0.0           | -               |
| 20.9091 | 1150 | 0.0           | -               |
| 21.8182 | 1200 | 0.0           | -               |
| 22.7273 | 1250 | 0.0           | -               |
| 23.6364 | 1300 | 0.0           | -               |
| 24.5455 | 1350 | 0.0           | -               |
| 25.4545 | 1400 | 0.0           | -               |
| 26.3636 | 1450 | 0.0           | -               |
| 27.2727 | 1500 | 0.0           | -               |
| 28.1818 | 1550 | 0.0           | -               |
| 29.0909 | 1600 | 0.0           | -               |
| 30.0    | 1650 | 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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