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: 셰프마스터 쉐프마스터 식용색소 2.3oz 온스 베이킹 슬라임 마카롱색소 퍼플 2.3oz 위베이크
- text: 행복한 쌀잉어빵 반죽 5kg 팥앙금 3kg 행복유통
- text: 셰프마스터 쉐프마스터 식용색소 0.7oz 리쿠아젤 마카롱색소 반액상타입 아보카도 위베이크
- text: 쫄깃한호떡가루 2.5kg 업소용 씨앗호떡 찹쌀반죽 밀가루 파우더  번개호랑이
- text: 퀄리티 스프링클 크리스마스 이브 63g 케이크 원형 쿠키 데코 6.발렌타인 넌패럴 스프링클(NEW) 위베이크
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: 0.8174651303820497
      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:** 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                                                                                                                                                         |
|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 3.0   | <ul><li>'찹쌀호떡믹스 400g 5개  오브젝티브'</li><li>'신진 찹쌀호떡가루 2.5Kg 호떡믹스  퍼스트'</li><li>'찹쌀호떡믹스 400g 10개 묶음배송가능 옵션9.\xa0오븐용깨찰빵믹스 500g EY 인터내셔널'</li></ul>                    |
| 0.0   | <ul><li>'브레드가든 바닐라에센스 59ml  주식회사 몬즈컴퍼니'</li><li>'선인 냉동레몬제스트 500g 레몬껍질 선인 냉동레몬제스트 500g 레몬껍질 아이은하'</li><li>'샤프 인스턴트 이스트 골드 500g 샤프 이스트 골드 500g 주식회사 맘쿠킹'</li></ul> |
| 2.0   | <ul><li>'곰표 와플믹스 1kg x 4팩  코스트코나'</li><li>'동원비셰프 스위트사워믹스1kg  엠디에스마케팅 주식회사'</li><li>'CJ 백설 붕어빵믹스 10kg [맛있는] [좋아하는]간편  로이스'</li></ul>                              |
| 1.0   | <ul><li>'오뚜기 베이킹소다 400g  지윤 주식회사'</li><li>'밥스레드밀 파우더 397g 베이킹  글로벌피스'</li><li>'Anthony s 유기농 요리 등급 코코아 파우더 1 lb  프로마스터'</li></ul>                                |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.8175 |

## 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_fd17")
# Run inference
preds = model("행복한 쌀잉어빵 반죽 5kg 팥앙금 3kg 행복유통")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 9.2    | 22  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 50                    |
| 1.0   | 50                    |
| 2.0   | 50                    |
| 3.0   | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0312  | 1    | 0.4064        | -               |
| 1.5625  | 50   | 0.1639        | -               |
| 3.125   | 100  | 0.003         | -               |
| 4.6875  | 150  | 0.0003        | -               |
| 6.25    | 200  | 0.0001        | -               |
| 7.8125  | 250  | 0.0001        | -               |
| 9.375   | 300  | 0.0001        | -               |
| 10.9375 | 350  | 0.0           | -               |
| 12.5    | 400  | 0.0           | -               |
| 14.0625 | 450  | 0.0           | -               |
| 15.625  | 500  | 0.0           | -               |
| 17.1875 | 550  | 0.0           | -               |
| 18.75   | 600  | 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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