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: 앙카라 스파게티면 5kg  지윤 주식회사
- text: 삼양 까르보불닭볶음면 130gx4봉-1팩/멀티라면 오뚜기_멀티 스낵면 108gx5봉-1팩 단비마켓
- text: '[오뚜기] 진라면 순한맛 5개 외 라면 멀티팩 인기상품 골라담기 (구매금액별 추가 할인) 삼양_삼양라면 5개 유진통상'
- text: 풀무원 바릴라 아티장 파스타면(130g) 20봉 (통밀스파게티/스파게티/링귀니/푸실리) 통밀스파게티면X20봉 메가글로벌001
- text: 농심 짜파게티 5개입 멀티팩(유통기한 24년 8월 1일 이후) 농심 신라면 건면 5입 멀티팩 세계로마트
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: 0.9463862379186676
      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>'동원 면발의신 얼큰칼국수268g x 4 코스트코 650449 상품 상세페이지 참조_268g x 4 탑럭셔리3'</li><li>'[청정원] 두부로만든 콩담백면 택1(옵션선택) 1.비빔 380g(2인) 아센드라도'</li><li>'라리 펜네 500g  (유) 싱싱채소 그린팜'</li></ul> |
| 0.0   | <ul><li>'팔도 비빔면딸기 135g (5개입) x 1팩  주식회사 디씽컴퍼니'</li><li>'오뚜기 열 라면 120g 5개  서신빠마켓'</li><li>'농심 오징어짬뽕컵 67g x 6개  으쓱몰'</li></ul>                                                |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.9464 |

## 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_fd7")
# Run inference
preds = model("앙카라 스파게티면 5kg  지윤 주식회사")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 4   | 8.94   | 20  |

| 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    | 0.394         | -               |
| 3.125  | 50   | 0.0229        | -               |
| 6.25   | 100  | 0.0002        | -               |
| 9.375  | 150  | 0.0           | -               |
| 12.5   | 200  | 0.0           | -               |
| 15.625 | 250  | 0.0           | -               |
| 18.75  | 300  | 0.0           | -               |

### 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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