README.md

---
base_model: mini1013/master_domain
library_name: setfit
metrics:
- metric
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: '[10%+복수620]국내생산 남자여자 최대15켤레 페이크삭스 실리콘 덧신 양말 학생 무지 25_챠밍레이스실리콘_여성_베이지(4켤레)
    발장난양말'
- text: W616 따뜻한 두꺼운 순면 통파일 무지 긴 양말 여자 남자 빅사이즈 수면 겨울 덧신 니삭스 W432 골지 통파일 덧신_S(225-245mm)_블랙
    삭스에이
- text: '[2차 11/14 예약배송][23FW] HEMISH LEG WARMER - MELANGE GREY MELANGE GREY_FREE
    주식회사 타입스(Types Co.,Ltd)'
- text: 도톰한 면두올 양말 국내생산/중목/장목/스니커즈/패션/학생 25~26_26.남녀 기모덧신_여)2켤레 / 블랙 투투삭스
- text: 도톰 엄지 양말 발가락 여 타비 삭스 기모 보온 컬러 여자 두꺼운 무지 연브라운 김민주
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: metric
      value: 0.7735123253257968
      name: Metric
---

# 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:** 2 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>'자전거 등산 골프 겨울 발 다리토시 레그워머 브라운 디플코리아 (Digital Plus Korea)'</li><li>'국산 면 탁텔 겨울 방한 팔 다리 수면 토시 발 임산부 산후용품 수족냉증 겨울 방한 보온 기본 수면토시 그레이 세자매 양말'</li><li>'세븐다스 여자 레그워머 수면 여성 발토시 겨울 보온 SD001 그레이_FREE 아이보리'</li></ul> |
| 0.0   | <ul><li>'[매장발송] 마리떼 11/6 배송 3PACK EMBROIDERY SOCKS multi OS 와이에스마켓'</li><li>'에브리데이 플러스 쿠션 트레이닝 크루 삭스(3켤레) SX6888-100 024 '</li><li>'[롯데백화점]언더아머(백) 유니섹스 UA 코어 쿼터 양말 - 3켤레 1358344-100 1.LG 롯데백화점_'</li></ul>          |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.7735 |

## 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_ac8")
# Run inference
preds = model("도톰 엄지 양말 발가락 여 타비 삭스 기모 보온 컬러 여자 두꺼운 무지 연브라운 김민주")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 4   | 10.82  | 24  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 50                    |
| 1.0   | 50                    |

### Training Hyperparameters
- batch_size: (512, 512)
- num_epochs: (20, 20)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 40
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0625 | 1    | 0.4226        | -               |
| 3.125  | 50   | 0.0022        | -               |
| 6.25   | 100  | 0.0001        | -               |
| 9.375  | 150  | 0.0001        | -               |
| 12.5   | 200  | 0.0001        | -               |
| 15.625 | 250  | 0.0001        | -               |
| 18.75  | 300  | 0.0001        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0.dev0
- Sentence Transformers: 3.1.1
- Transformers: 4.46.1
- PyTorch: 2.4.0+cu121
- Datasets: 2.20.0
- Tokenizers: 0.20.0

## 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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