---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 집업 여자 요가복 상의 필라테스복 긴팔 운동복 상의 스포츠/레저>요가/필라테스>요가복>상의
- text: 요가 홈트 스트랩 벨트 필라테스 명상 레슨 플라잉 02 3세대 업그레이드 쿠션 스포츠/레저>요가/필라테스>기타요가용품
- text: 스파인코렉터 홈트 가정용 운동 교정 허리 마사지기 스포츠/레저>요가/필라테스>필라테스
- text: KKJN 남자반팔상의요가복 NT1105 스포츠/레저>요가/필라테스>요가복>상의
- text: 필라테스 척추 스트레칭 스파인코렉터 허리 홈트 요가 스포츠/레저>요가/필라테스>필라테스
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:** 5 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                                                                                                                                                                                                                                    |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 4.0   | <ul><li>'기구 필라테스 체어 스프링 스트레칭 홈트 스텝체어 스포츠/레저>요가/필라테스>필라테스'</li><li>'체어 필라테스 기구 대형 장비 코어 운동 전신 홈짐 스포츠/레저>요가/필라테스>필라테스'</li><li>'집에서하는필라테스 레더바렐 레더배럴 원목 스트레칭 스포츠/레저>요가/필라테스>필라테스'</li></ul>                                                    |
| 2.0   | <ul><li>'밸런시스 NEW NR1 센터라인 요가매트 6.3mm 스포츠/레저>요가/필라테스>요가매트'</li><li>'아디다스 피트니스 매트 ADYG-10010 스포츠/레저>요가/필라테스>요가매트'</li><li>'듀잇 매트 + 스트랩 SET 스포츠/레저>요가/필라테스>요가매트'</li></ul>                                                                    |
| 3.0   | <ul><li>'언더아머 커리 플리스 스웨트 팬츠 1374299001 스포츠/레저>요가/필라테스>요가복>하의'</li><li>'로라벨 무르 니트 필라테스복 여성 요가복 필라테스 요가 상의 옷 커버업 운동복 스포츠/레저>요가/필라테스>요가복>상의'</li><li>'데비웨어 여성 요가복 블랙 필라테스 서포트티 반팔티 DEVI-T0058 스포츠/레저>요가/필라테스>요가복>상의'</li></ul>                 |
| 0.0   | <ul><li>'그라데이션 플라잉 요가 해먹 고탄성 스포츠/레저>요가/필라테스>기타요가용품'</li><li>'자세 폴 요가 스트랩 - 스틱 벨트 스포츠/레저>요가/필라테스>기타요가용품'</li><li>'고정 공중 해먹 플라잉요가 요가 로프 천 스튜디오 스트레칭 필라테스 홈트 스포츠/레저>요가/필라테스>기타요가용품'</li></ul>                                                  |
| 1.0   | <ul><li>'요가 스트레칭 도구 요가링 필라테스링 허벅지 괄약근 다리 케겔운동 남성용 하체 힙업 항문 원형 팔 스포츠/레저>요가/필라테스>요가링/필라테스링'</li><li>'홈트 종아리운동 근육풀기용 혈액순환 하드 요가링 마사지 어깨결림 필라테스 스트레칭 스포츠/레저>요가/필라테스>요가링/필라테스링'</li><li>'홈트 운동 스트레칭 헬스 필라테스링 스포츠/레저>요가/필라테스>요가링/필라테스링'</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_sl24")
# Run inference
preds = model("KKJN 남자반팔상의요가복 NT1105 스포츠/레저>요가/필라테스>요가복>상의")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 8.2029 | 17  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 70                    |
| 1.0   | 70                    |
| 2.0   | 70                    |
| 3.0   | 70                    |
| 4.0   | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0145  | 1    | 0.4827        | -               |
| 0.7246  | 50   | 0.4432        | -               |
| 1.4493  | 100  | 0.0881        | -               |
| 2.1739  | 150  | 0.0004        | -               |
| 2.8986  | 200  | 0.0           | -               |
| 3.6232  | 250  | 0.0           | -               |
| 4.3478  | 300  | 0.0           | -               |
| 5.0725  | 350  | 0.0           | -               |
| 5.7971  | 400  | 0.0           | -               |
| 6.5217  | 450  | 0.0           | -               |
| 7.2464  | 500  | 0.0           | -               |
| 7.9710  | 550  | 0.0           | -               |
| 8.6957  | 600  | 0.0           | -               |
| 9.4203  | 650  | 0.0           | -               |
| 10.1449 | 700  | 0.0           | -               |
| 10.8696 | 750  | 0.0           | -               |
| 11.5942 | 800  | 0.0           | -               |
| 12.3188 | 850  | 0.0           | -               |
| 13.0435 | 900  | 0.0           | -               |
| 13.7681 | 950  | 0.0           | -               |
| 14.4928 | 1000 | 0.0           | -               |
| 15.2174 | 1050 | 0.0           | -               |
| 15.9420 | 1100 | 0.0           | -               |
| 16.6667 | 1150 | 0.0           | -               |
| 17.3913 | 1200 | 0.0           | -               |
| 18.1159 | 1250 | 0.0           | -               |
| 18.8406 | 1300 | 0.0           | -               |
| 19.5652 | 1350 | 0.0           | -               |
| 20.2899 | 1400 | 0.0           | -               |
| 21.0145 | 1450 | 0.0           | -               |
| 21.7391 | 1500 | 0.0           | -               |
| 22.4638 | 1550 | 0.0           | -               |
| 23.1884 | 1600 | 0.0           | -               |
| 23.9130 | 1650 | 0.0           | -               |
| 24.6377 | 1700 | 0.0           | -               |
| 25.3623 | 1750 | 0.0           | -               |
| 26.0870 | 1800 | 0.0           | -               |
| 26.8116 | 1850 | 0.0           | -               |
| 27.5362 | 1900 | 0.0           | -               |
| 28.2609 | 1950 | 0.0           | -               |
| 28.9855 | 2000 | 0.0           | -               |
| 29.7101 | 2050 | 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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