---
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: 백설 찰밀가루 3Kg  에프엠에스인터내셔널 주식회사
- text: 퀘이커 마시는오트밀 그래인 50g 20개 오트&봄딸기50gx10개_오트&우리쌀 50gx10개 (주)태풍
- text: CJ제일제당 백설 강력밀가루 2.5kg  둘레푸드
- text: 이츠웰 맛있는 튀김가루 1kg / CJ프레시웨이  청신호
- text: 피플스 퀵오트밀 500gx2 (1kg) 귀리 07.퀵오트500g+뮤즐리500g 피플스(Peoples)
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: 0.9629787234042553
      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:** 11 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                                                                                                                                                                                                                              |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 7.0   | <ul><li>'[플라하반] 유기농 포리지 500g 외 2종 롤드오트 압착귀리 유기농 포리지 280g 주식회사 수성인터내셔널'</li><li>'포스트 화이버 오트밀 오리지날 350g  다복상사'</li><li>'오트밀(식사용) 1kg/이든타운/오트밀/오트밀죽/oatmeal/압착귀리/곡류/곡물/시리얼/씨리얼/후레이크/생식/선식/건강식/두유/우유/제과/제빵/쿠키/재료/식사대용/요거트  드랍쉽'</li></ul> |
| 0.0   | <ul><li>'볶은 검은깨 분말 가루 국내산 300g 검정깨 블랙푸드 검은콩청국장환 200g 농업회사법인 주식회사 두손애약초'</li><li>'볶은 검은깨 분말 가루 국내산 300g 검정깨 블랙푸드 검은콩검은깨환 210g 농업회사법인 주식회사 두손애약초'</li><li>'국산 냉풍건조 아로니아분말 500g [분말]아로니아분말 500g x 2팩 농업회사법인 청정산들해(주)'</li></ul>          |
| 1.0   | <ul><li>'뚜레반 17곡 미숫가루 1kg B_청정원 홍초 자몽900ml 무한상사'</li><li>'뚜레반 17곡 미숫가루 1kg C_뚜레반 콩국수용 콩가루850g 무한상사'</li><li>'뚜레반 17곡 미숫가루A+1kg  주식회사 삼부'</li></ul>                                                                                    |
| 3.0   | <ul><li>'[대한제분]곰표부침가루1kg / 곰표튀김가루1kg 감사 곰표부침가루1kg 동아식품'</li><li>'오뚜기 나눔7호 직원 거래처 명절준비 선물세트  제이엔팩토리'</li><li>'큐원 쫄깃한 참 부침 가루 1kg 가정 업소 호박 파 전  전가네TMG'</li></ul>                                                                       |
| 6.0   | <ul><li>'프리미엄 아몬드가루 1kg 95% 아몬드분말 아몬드파우더 프리미엄 아몬드분말(95%) 1kg 대륙유통'</li><li>'너츠빌 캘리포니아 아몬드 분말 가루 파우더 1kg 아몬드 슬라이스 1kg (주)엠디에프앤'</li><li>'너츠빌 캘리포니아 아몬드 분말 가루 파우더 1kg 아몬드 분말 100% 1kg (주)엠디에프앤'</li></ul>                               |
| 8.0   | <ul><li>'사조해표 찹쌀가루 350g  건우푸드'</li><li>'사조 해표 찹쌀가루 350g 감자전분 350g 주식회사 더 골든트리'</li><li>'해표 찹쌀가루 350g-1개  에이치엠몰(HM mall)'</li></ul>                                                                                                    |
| 10.0  | <ul><li>'해표 튀김가루 1kg/부침요리/전 해표 튀김가루 1kg 단비마켓'</li><li>'CJ제일제당 백설 치킨 튀김가루 1kg  바름푸드'</li><li>'CJ제일제당 백설 튀김가루 1kg 1)튀김가루 태성유통'</li></ul>                                                                                                |
| 4.0   | <ul><li>'신일 냉동 골드빵가루 2kg  (주)우주식품디씨오피'</li><li>'오뚜기 빵가루 1KG 자취 대용량 식자재 선물 튀김 제사 명절 부침개 간식  하나칭구'</li><li>'오뚜기 빵가루 200g  이고지고'</li></ul>                                                                                               |
| 2.0   | <ul><li>'백설 박력밀가루 1kg (박력분)  주식회사 몬즈컴퍼니'</li><li>'아티장 밀가루 T55 20KG 백설  베이킹스타'</li><li>'박력밀가루(큐원 1K)  썬샤인웍스'</li></ul>                                                                                                                 |
| 5.0   | <ul><li>'[대두식품] 강력쌀가루(국산) 15kg  (주)대두식품서울지점'</li><li>'싸리재 유기농 습식 쌀가루 [ 백미 멥쌀가루 1kg ] 떡만들기 베이킹 비건요리 무염백미찹쌀가루 1kg 농업회사법인콩사랑유한회사'</li><li>'햇쌀마루 박력쌀가루 3kg  이캔유통'</li></ul>                                                               |
| 9.0   | <ul><li>'뚜레반 날콩가루 1kg  (주)울산팡'</li><li>'복만네 콩국수용 콩가루 850g 05.해늘이볶은콩가루1kg 바른에프에스'</li><li>'[복만네] 콩국수용 콩가루 850g / 콩국 선식  (주)유영유통'</li></ul>                                                                                             |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.9630 |

## 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_fd0")
# Run inference
preds = model("CJ제일제당 백설 강력밀가루 2.5kg  둘레푸드")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 4   | 8.9308 | 24  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 50                    |
| 1.0   | 22                    |
| 2.0   | 50                    |
| 3.0   | 50                    |
| 4.0   | 50                    |
| 5.0   | 32                    |
| 6.0   | 18                    |
| 7.0   | 50                    |
| 8.0   | 26                    |
| 9.0   | 50                    |
| 10.0  | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0143  | 1    | 0.4619        | -               |
| 0.7143  | 50   | 0.2999        | -               |
| 1.4286  | 100  | 0.1066        | -               |
| 2.1429  | 150  | 0.0721        | -               |
| 2.8571  | 200  | 0.0457        | -               |
| 3.5714  | 250  | 0.03          | -               |
| 4.2857  | 300  | 0.0045        | -               |
| 5.0     | 350  | 0.002         | -               |
| 5.7143  | 400  | 0.004         | -               |
| 6.4286  | 450  | 0.002         | -               |
| 7.1429  | 500  | 0.0077        | -               |
| 7.8571  | 550  | 0.002         | -               |
| 8.5714  | 600  | 0.006         | -               |
| 9.2857  | 650  | 0.0019        | -               |
| 10.0    | 700  | 0.0001        | -               |
| 10.7143 | 750  | 0.0001        | -               |
| 11.4286 | 800  | 0.0001        | -               |
| 12.1429 | 850  | 0.0           | -               |
| 12.8571 | 900  | 0.0           | -               |
| 13.5714 | 950  | 0.0           | -               |
| 14.2857 | 1000 | 0.0           | -               |
| 15.0    | 1050 | 0.0           | -               |
| 15.7143 | 1100 | 0.0           | -               |
| 16.4286 | 1150 | 0.0           | -               |
| 17.1429 | 1200 | 0.0           | -               |
| 17.8571 | 1250 | 0.0           | -               |
| 18.5714 | 1300 | 0.0           | -               |
| 19.2857 | 1350 | 0.0           | -               |
| 20.0    | 1400 | 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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