README.md

---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 복싱 권투 어린이 글러브 아동용 킥 샌드백 스포츠/레저>권투>글러브
- text: 이지핏 흡착형 스탠딩 샌드백 펀치볼 격투기 복싱 스포츠/레저>권투>샌드백
- text: Spall Pro US Dino 남성 여성용 복싱 글러브 - 프로 트레이닝 스파링 펀칭 스포츠/레저>권투>글러브
- text: 에버라스트 에버레스트 혼합 격투기 헤비 백 글러브 L 384355 스포츠/레저>권투>글러브
- text: 레예스글러브 장갑 펀치 가죽 스파링 어린이용 PU 훈련 스포츠 백 스포츠/레저>권투>글러브
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>'태권도 발차기 고정식 미트 격투기 복싱 보조 장비 스포츠/레저>권투>미트'</li><li>'태권도 발차기 미트 킥 가정용 연습 샌드백 훈련 장비 블랙화이트 업그레이드 - 이중층쿠션 킥트레이닝 스포츠/레저>권투>미트'</li><li>'빅산 PU펀치볼-레드 스포츠/레저>권투>미트'</li></ul> |
| 2.0   | <ul><li>'스파트 샌드백걸이대 권투 복싱장 SFC-W706 스포츠/레저>권투>샌드백'</li><li>'hale 뮤직복싱머신 샌드백 펀칭백 펀치 스마트 스포츠/레저>권투>샌드백'</li><li>'스타스포츠 스타 팝업 디펜더 구기종목 더미및타겟으로활용 XU400 스포츠/레저>권투>샌드백'</li></ul>   |
| 0.0   | <ul><li>'이사미 글러브 여자 스파링 복싱 킥복싱 MMA 프리 SS801 스포츠/레저>권투>글러브'</li><li>'베넘 Venum 엘리트 복싱 글러브 스포츠/레저>권투>글러브'</li><li>'아식스 ASICS 남성용 라이벌 레슬링 싱글렛 스포츠/레저>권투>글러브'</li></ul>             |
| 3.0   | <ul><li>'운동 장갑 다이어트 복싱 격투기 스파링 글러브 핸드랩 권투 주짓수 스포츠/레저>권투>핸드랩'</li><li>'코어 퀵 핸드랩 복싱용품 보호용품 에버라스트핸드랩 스포츠/레저>권투>핸드랩'</li><li>'에버라스트 프로 핸드랩 스포츠/레저>권투>핸드랩'</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_sl2")
# Run inference
preds = model("복싱 권투 어린이 글러브 아동용 킥 샌드백 스포츠/레저>권투>글러브")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 2   | 9.5857 | 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    | 0.4882        | -               |
| 0.9091  | 50   | 0.4817        | -               |
| 1.8182  | 100  | 0.133         | -               |
| 2.7273  | 150  | 0.0004        | -               |
| 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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