---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 탁구러버 보호필름 탁구채 러버 보호스티커 랜덤 스포츠/레저>탁구>기타탁구용품
- text: 엑시옴 탁구상의 토마스 탁구유니폼 티셔츠 스포츠/레저>탁구>탁구의류
- text: 닛타쿠 탁구러버 스페셜리스트 러버 스피드 라지볼 스포츠/레저>탁구>기타탁구용품
- text: 아식스 아식스 어택 하이퍼비트 4 탁구화 인도어 레슬링 복싱 트레이닝 1073A056 스포츠/레저>탁구>탁구화
- text: 미즈노 메달 7 탁구 신유빈탁구화 웨이브드라이브 마롱탁구화 체대입시화 스포츠/레저>탁구>탁구화
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:** 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                                                                                                                                                                                                  |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 2.0   | <ul><li>'제맥스 박서 야외용 탁구대 스포츠/레저>탁구>탁구대'</li><li>'챔피온 H-50 탁구대 스포츠/레저>탁구>탁구대'</li><li>'휠러스 미니 탁구대 스포츠/레저>탁구>탁구대'</li></ul>                                                                                  |
| 3.0   | <ul><li>'버터플라이 RDJ S1 탁구라켓 스포츠/레저>탁구>탁구라켓'</li><li>'엑시옴 하야부사 ZL PRO 탁구라켓 FL 스포츠/레저>탁구>탁구라켓'</li><li>'고집통 나노100 탁구라켓 스포츠/레저>탁구>탁구라켓'</li></ul>                                                             |
| 5.0   | <ul><li>'미즈노 탁구복 게임 팬츠 탁구 유니섹스 Mizuno 82JBA10009 스포츠/레저>탁구>탁구의류'</li><li>'버터플라이 윈로고 티셔츠 스포츠/레저>탁구>탁구의류'</li><li>'미즈노 탁구복 게임 바지 탁구웨어 426388 82JB900109 스포츠/레저>탁구>탁구의류'</li></ul>                           |
| 6.0   | <ul><li>'리닝 탁구화 23 아틀란티스 보아 라임 - 바운스플러스 슈퍼라이트 시리즈 스포츠/레저>탁구>탁구화'</li><li>'아식스 탁구화 ATTACK DOMINATE FF 2 1073A010-003 스포츠/레저>탁구>탁구화'</li><li>'미즈노 탁구화 웨이브 드라이브 EL 경량성 쿠션성 81GA2001 스포츠/레저>탁구>탁구화'</li></ul> |
| 0.0   | <ul><li>'엑시옴 솔라이트 SOLITE 백팩 스포츠/레저>탁구>기타탁구용품'</li><li>'Dawei 다웨이 미디엄 핌플러버 스폰지버전 - 탁구러버 388C-1 스포츠/레저>탁구>기타탁구용품'</li><li>'도닉 탁구 러버 블루스톰 프로 스포츠/레저>탁구>기타탁구용품'</li></ul>                                     |
| 1.0   | <ul><li>'비코트 슈퍼 ABS 40+ 탁구공 스포츠/레저>탁구>탁구공'</li><li>'안드로 스피드볼 Mi 1 탁구공 스포츠/레저>탁구>탁구공'</li><li>'오로라 AURORA 3성 BST 시합구 스포츠/레저>탁구>탁구공'</li></ul>                                                              |
| 4.0   | <ul><li>'탁구러버 티바 에볼루션 MX-P 스포츠/레저>탁구>탁구러버'</li><li>'티바 하이브리드 K3 HYBRID K3 평면 탁구러버 스포츠/레저>탁구>탁구러버'</li><li>'엑시옴 탁구러버 베가 프로 스포츠/레저>탁구>탁구러버'</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_sl29")
# Run inference
preds = model("엑시옴 탁구상의 토마스 탁구유니폼 티셔츠 스포츠/레저>탁구>탁구의류")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 6.8776 | 14  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 70                    |
| 1.0   | 70                    |
| 2.0   | 25                    |
| 3.0   | 70                    |
| 4.0   | 9                     |
| 5.0   | 70                    |
| 6.0   | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0133  | 1    | 0.4688        | -               |
| 0.6667  | 50   | 0.5004        | -               |
| 1.3333  | 100  | 0.1817        | -               |
| 2.0     | 150  | 0.0186        | -               |
| 2.6667  | 200  | 0.0024        | -               |
| 3.3333  | 250  | 0.0009        | -               |
| 4.0     | 300  | 0.0001        | -               |
| 4.6667  | 350  | 0.0           | -               |
| 5.3333  | 400  | 0.0           | -               |
| 6.0     | 450  | 0.0           | -               |
| 6.6667  | 500  | 0.0           | -               |
| 7.3333  | 550  | 0.0           | -               |
| 8.0     | 600  | 0.0           | -               |
| 8.6667  | 650  | 0.0           | -               |
| 9.3333  | 700  | 0.0           | -               |
| 10.0    | 750  | 0.0           | -               |
| 10.6667 | 800  | 0.0           | -               |
| 11.3333 | 850  | 0.0           | -               |
| 12.0    | 900  | 0.0           | -               |
| 12.6667 | 950  | 0.0           | -               |
| 13.3333 | 1000 | 0.0           | -               |
| 14.0    | 1050 | 0.0           | -               |
| 14.6667 | 1100 | 0.0           | -               |
| 15.3333 | 1150 | 0.0           | -               |
| 16.0    | 1200 | 0.0           | -               |
| 16.6667 | 1250 | 0.0           | -               |
| 17.3333 | 1300 | 0.0           | -               |
| 18.0    | 1350 | 0.0           | -               |
| 18.6667 | 1400 | 0.0           | -               |
| 19.3333 | 1450 | 0.0           | -               |
| 20.0    | 1500 | 0.0           | -               |
| 20.6667 | 1550 | 0.0           | -               |
| 21.3333 | 1600 | 0.0           | -               |
| 22.0    | 1650 | 0.0           | -               |
| 22.6667 | 1700 | 0.0           | -               |
| 23.3333 | 1750 | 0.0           | -               |
| 24.0    | 1800 | 0.0           | -               |
| 24.6667 | 1850 | 0.0           | -               |
| 25.3333 | 1900 | 0.0           | -               |
| 26.0    | 1950 | 0.0           | -               |
| 26.6667 | 2000 | 0.0           | -               |
| 27.3333 | 2050 | 0.0           | -               |
| 28.0    | 2100 | 0.0           | -               |
| 28.6667 | 2150 | 0.0           | -               |
| 29.3333 | 2200 | 0.0           | -               |
| 30.0    | 2250 | 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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