---
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: 동원 덴마크 구워먹는 치즈 후라이드 갈릭 125g x 3개  007스테이지스
- text: 앵커크림치즈 1박스 (1kg x 12개) 앵커크림치즈 1박스 (1kg x 12개) (주)비오비
- text: 홉라 무가당 휘핑크림 1L 2개세트  마켓이
- text: 매일 상하치즈 리코타치즈 200g 2개 냉장배송  대명유통
- text: 홉라 생크림 무가당 1L 휘핑크림 베이킹 쿠킹크림 1000ml 홉라 생크림 무가당 1L + 아이스박스 (주)비오비
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: 0.9220726783310902
      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:** 6 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                                                                                                                                                                                                                  |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 5.0   | <ul><li>'[아이스박스무료] 선인 DB 휘핑크림 1L 무가당 혼합 생크림  재이F&B'</li><li>'이탈리아 홉라 식물성 생크림 500ml 6개 무가당 홉라 홈라 크림  알이즈웰'</li><li>'카파 포모나 휘핑 스프레이 500g 와플 크림 딸기향/휘핑 스프레이 500g 디씨즈(This is)'</li></ul>                                     |
| 1.0   | <ul><li>'오뚜기 버터후레시 48개(아이스박스)/일회용버터 오뚜기 딸기잼 디스펜팩 40개+메이플시럽 40 박길용'</li><li>'버터린 롯데 450g 마늘향오일 갈릭버터오일  (주)인벨'</li><li>'[본사직송] 라꽁비에뜨 가염 무염 꽃소금 버터 450g (15g x 30개) 11/15(수)배송예정_라꽁비에뜨-가염 450g (30개입) 인에이블 코리아(주)'</li></ul> |
| 3.0   | <ul><li>'인도네시아 인도 밀크 스위트드컨덴센 팩 545g 연유  동원무역(이마트24 감천네거리점)'</li><li>'매일연유 5kg 대용량 x 2개 연유  카페스토리(CAFE STORY)'</li><li>'누티 크리머 스위텐드 연유 시럽 385g x 8개  클루'</li></ul>                                                         |
| 0.0   | <ul><li>'오뚜기 파운드 마아가린(벌크) 9kg  피치피치몰'</li><li>'오뚜기 쿠키 옥수수마가린 200Gx2 1세트 제과 제빵 토스트  방글방글마켓'</li><li>'Whirl Admiration Pro Fry 액체 쇼트닝 튀김용 3.6kg 8파운드  포커스라이프'</li></ul>                                                     |
| 2.0   | <ul><li>'밀락골드 1L 제품수량선택 에스제이푸드(SJ FOOD)'</li><li>'[구매전 긴급공지 필독]1217. 뉴골드라벨 - 한박스(1030g x 12개)  베이킹도전'</li><li>'밀락골드 1L 아이스박스필수구매 에스제이푸드(SJ FOOD)'</li></ul>                                                               |
| 4.0   | <ul><li>'상하 샐러드용 슈레드치즈 210g X 1개 종이박스포장 오하'</li><li>'끼리 크림치즈 스프레드 플레인 x 4개 베이글 발라 먹는 치즈 토스트 끼리 크림치즈 스프레드 플레인 4개 더팜'</li><li>'데르뜨 롤케이크 선물세트 소잘우유 초코크림 380g 1개 우유크림 360g 냉동  오씨홀딩스'</li></ul>                               |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.9221 |

## 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_fd13")
# Run inference
preds = model("홉라 무가당 휘핑크림 1L 2개세트  마켓이")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 9.5067 | 18  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 50                    |
| 1.0   | 50                    |
| 2.0   | 50                    |
| 3.0   | 50                    |
| 4.0   | 50                    |
| 5.0   | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0213  | 1    | 0.4249        | -               |
| 1.0638  | 50   | 0.2783        | -               |
| 2.1277  | 100  | 0.0747        | -               |
| 3.1915  | 150  | 0.0734        | -               |
| 4.2553  | 200  | 0.0368        | -               |
| 5.3191  | 250  | 0.0373        | -               |
| 6.3830  | 300  | 0.0003        | -               |
| 7.4468  | 350  | 0.0001        | -               |
| 8.5106  | 400  | 0.0001        | -               |
| 9.5745  | 450  | 0.0001        | -               |
| 10.6383 | 500  | 0.0           | -               |
| 11.7021 | 550  | 0.0           | -               |
| 12.7660 | 600  | 0.0           | -               |
| 13.8298 | 650  | 0.0           | -               |
| 14.8936 | 700  | 0.0           | -               |
| 15.9574 | 750  | 0.0           | -               |
| 17.0213 | 800  | 0.0           | -               |
| 18.0851 | 850  | 0.0           | -               |
| 19.1489 | 900  | 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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