---
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: 한끼스토리 딸기드레싱 500g 10개  한울마켓
- text: 맷돌표 뉴슈가 60g/ 20개  (주)디엔제이
- text: 하회마을 쌈장 14kg 업소용 대용량  물레푸드
- text: 화가장가평발효과학 국산콩청국장 120g16팩  화가장 주식회사
- text: 춘장(삼화 300g) 4개 식자재 업소용 대용량  더착한컴퍼니
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: 0.8727670433831571
      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:** 9 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                                                                                                                                                                                                                          |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 8.0   | <ul><li>'아이스티음료 복숭아음료 립톤 음료 베이스 가루 업소 대용량 907g  온달이'</li><li>'죽순캔(진양 400g)X4 진양 400g)X4 프렌들리 컴퍼니'</li><li>'커피믹스(맥심 2.04k) X6 모카골드  오구오구(5959)'</li></ul>                                                                          |
| 0.0   | <ul><li>'청정원 장아찌 간장소스 1.7L  착한사람들'</li><li>'몽고 송표 골드 간장 1.5L  몽고식품(주)창원1공장'</li><li>'샘표 맛간장 조림볶음용  보니따엠'</li></ul>                                                                                                                |
| 3.0   | <ul><li>'으뜸 낫또 제주콩 생나또 53 g 특허기술로 만든 생 청국장 실이많은 생낫또 24 팩 혼합구성_생낫또 18 개 + 하나또 18 개 (주)으뜸엘엔에스'</li><li>'국산콩100g 12개 일본장인전수 수제 가정생낫또 나또 사또 검정콩 쥐눈이 대용량 검정콩100g 10개_간장360ml 가정생청국장'</li><li>'청정 제주콩 생 낫또 36개  주식회사 네오넥스글로벌'</li></ul> |
| 4.0   | <ul><li>'CJ 해찬들 그대로 된장찌개양념 450gx3 고깃집 된장찌개용 차돌 조개 코스트코 1021460 4 바지락꽃게 3개 까까아일랜드'</li><li>'[2+1] 해찬들 물로만 끓여도 차돌 된장찌개 양념 450G 물로만 끓여도 차돌 된장찌개 450Gx3 메가글로벌001'</li><li>'[샘표]샘표 토장 900g  티디티유통'</li></ul>                           |
| 1.0   | <ul><li>'안동제비원 고추장 담그기 세트 (약7kg)[33628066]  (주)엔에스쇼핑'</li><li>'CJ 해찬들 태양초 알찬 고추장 6.5kg  리브웨이'</li><li>'청정원순창 현미 태양초 찰고추장, 2kg, 1개 2kg × 1개 2kg x 1개 카리스광클'</li></ul>                                                              |
| 6.0   | <ul><li>'샘표 쌈토장 450g  대성상사'</li><li>'참고을 신선한 쌈장 14kg 맛있는 쌈장 대용량 업소용 쌈장 지함 순창궁 양념 쌈장 14kg 우성수산'</li><li>'청정원 순창 쌈장골드 4.8kg  주식회사 푸드공공칠'</li></ul>                                                                                  |
| 5.0   | <ul><li>'콩마실 국산 메주 가루 (1kg 국산콩100%, 고추장용)  콩마실'</li><li>'고령 국산 메주 전통 국산콩메주 세트 5kg  장현식품'</li><li>'100%국산콩으로 만든 순창 전통메주 1덩이 1.2kg내외  열정농부'</li></ul>                                                                               |
| 7.0   | <ul><li>'고추명가 비빔냉면 소스 2kg 냉면 양념장 비냉 비빔장 국수 양념 다대기 식당업소용 대용량  이도'</li><li>'CJ 손맛 다담 안동찜닭 양념 220g 분식 식당 식자재 감칠맛 풍미 맛다시  제이지무역'</li><li>'연안식당 부추꼬막장 150g 앙념 비빔장 꼬막비빔밥 밥도둑 꼬막장 넉넉한 2인분  주식회사 디딤'</li></ul>                            |
| 2.0   | <ul><li>'오뚜기 가쓰오부시 장국 360ml 외 7종 01_가쓰오부시 장국 360ml 주식회사 삼부'</li><li>'면사랑 프리미엄 메밀장국 1.8L 모밀 소바 육수 장국 국수 찌개 만능 다시  문화벙커'</li><li>'뽕보감 조청 1000g  철원군농업기술센터'</li></ul>                                                                |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.8728 |

## 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_fd15")
# Run inference
preds = model("맷돌표 뉴슈가 60g/ 20개  (주)디엔제이")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 9.8578 | 26  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 50                    |
| 1.0   | 50                    |
| 2.0   | 50                    |
| 3.0   | 50                    |
| 4.0   | 50                    |
| 5.0   | 22                    |
| 6.0   | 50                    |
| 7.0   | 50                    |
| 8.0   | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0152  | 1    | 0.3728        | -               |
| 0.7576  | 50   | 0.2769        | -               |
| 1.5152  | 100  | 0.1245        | -               |
| 2.2727  | 150  | 0.0532        | -               |
| 3.0303  | 200  | 0.0532        | -               |
| 3.7879  | 250  | 0.0385        | -               |
| 4.5455  | 300  | 0.0052        | -               |
| 5.3030  | 350  | 0.0025        | -               |
| 6.0606  | 400  | 0.0004        | -               |
| 6.8182  | 450  | 0.0004        | -               |
| 7.5758  | 500  | 0.0005        | -               |
| 8.3333  | 550  | 0.0007        | -               |
| 9.0909  | 600  | 0.0002        | -               |
| 9.8485  | 650  | 0.0002        | -               |
| 10.6061 | 700  | 0.0001        | -               |
| 11.3636 | 750  | 0.0001        | -               |
| 12.1212 | 800  | 0.0001        | -               |
| 12.8788 | 850  | 0.0001        | -               |
| 13.6364 | 900  | 0.0001        | -               |
| 14.3939 | 950  | 0.0001        | -               |
| 15.1515 | 1000 | 0.0001        | -               |
| 15.9091 | 1050 | 0.0001        | -               |
| 16.6667 | 1100 | 0.0001        | -               |
| 17.4242 | 1150 | 0.0001        | -               |
| 18.1818 | 1200 | 0.0001        | -               |
| 18.9394 | 1250 | 0.0           | -               |
| 19.6970 | 1300 | 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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