---
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: 1883 시럽 1000ml 바닐라 3병 Vanilla 바닐라 달콤한푸린
- text: 모닌 바닐라 시럽 1000ml MONIN 홈카페 커피시럽 로스티드 헤이즐넛 700ml 아르타
- text: 리고 초코 시럽 585g 2개세트  (주)비앤씨인터내셔널
- text: 옳곡 국내산 피넛버터 땅콩잼 무첨가 땅콩버터 200g 크런치 스무스 03.스무스+크런치 조은스토어2
- text: 페레로 누텔라 헤이즐넛 코코아 스프레드 370g 5개 누텔라 헤이즐넛 코코아 스프레드 370g 5개 홈마트
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: 0.6548139319295457
      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:** 8 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                                                                                                                                                                                                        |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 6.0   | <ul><li>'아이언맥스 프로틴 쨈 스프레드 초코 아몬드 250g 2팩 IronMaxx  블레스윤'</li><li>'페레로 누텔라 헤이즐넛 코코아 스프레드 370g 3개 누텔라 헤이즐넛 코코아 스프레드 370g 3개 홈마트'</li><li>'누텔라 헤이즐넛 코코아 스프레드 370g x 2개 [라면] 봉지라면_오뚜기 짜슐랭 145g 20개 옐로우로켓'</li></ul> |
| 1.0   | <ul><li>'[가당딸기] 국산 냉동 가당딸기 2kg 아이스베리 (6개/박스)  주식회사 커피바바'</li><li>'복음자리 진심의 딸기 1kg 딸기청 🍓진심의 딸기 1kg 5개🍓 담다'</li><li>'초록원 과일잼 1kg x 2개 딸기잼 1021653 딸기잼1kg 블루베리잼1kg_파인애플망고잼1kg 앤디월드'</li></ul>                      |
| 5.0   | <ul><li>'Torani 무설탕 소스, 다크 초콜릿, 1.9L(64온스) 화이트 초콜릿_64 Fl Oz (Pack of 1) 저무리5'</li><li>'모카믹스 다크소스 초콜렛 2kg 1박스 6개 초코소스  엠씨컴퍼니 (주)'</li><li>'매일유업 테너소스 초콜렛 1.35kg 1병 카라멜 1.35kg 티피컨테이너'</li></ul>                  |
| 4.0   | <ul><li>'오뚜기 맛있는 사과쨈 300G 홈카페 식재료 토스트 브런치 캠핑 아이들 간식  봄날스토어'</li><li>'오뚜기 Light sugar 사과쨈 290g 4개  007스테이지스'</li><li>'[달콤한 맛있는] 밀크스프레드 얼그레이 235g [블루베리 딸기 사과 포도 버터맛]  레인보우'</li></ul>                            |
| 0.0   | <ul><li>'포모나 얼그레이 하이볼 시럽 밀크티 홍차 1000ml 06-포모나 카라멜 시럽 주식회사 커피창고'</li><li>'프프프베이커리 빵에 발라먹는 버터스프레드 얼그레이 맛 【1개】 허니 데칼컴퍼니(Decal Company)'</li><li>'매일 테너베이스 청포도 에이드 스무디 농축액 1.2kg 1022147 오렌지 1.2kg 가이던스'</li></ul>  |
| 3.0   | <ul><li>'LB 메이플시럽189ml(병) (N2)  주식회사 에스에스지닷컴'</li><li>'마누카 헬스 Manuka health 마누카 허니 MGO 250+ 시럽 100ml  K&G GmbH'</li><li>'시럽 초콜렛 네이처 컨트리  라몬제이'</li></ul>                                                        |
| 7.0   | <ul><li>'커피시럽 카페시럽 1.5L x2병 대상 롯데 파우더 커피 대상 로즈버드 그린티 파우더 500g 가루녹차 하늘담아'</li><li>'토라니 카라멜 미니 토핑용소스 468g / 카라멜마끼야또 카라멜라떼  (주)오케이푸드'</li><li>'1883 헤이즐넛시럽 1883 라임 시럽 1000ml 엔에프 컴퍼니'</li></ul>                    |
| 2.0   | <ul><li>'[신세계 가공](신세계본점)리고땅콩버터크리미 462g  주식회사 에스에스지닷컴'</li><li>'스키피 땅콩버터1.36kg 스키피 크리미 땅콩버터 2.27kg 두두유통'</li><li>'피비핏 버터 오리지널 파우더 피넛 프리 프로틴 글루텐 850g  에코프리'</li></ul>                                            |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.6548 |

## 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_fd16")
# Run inference
preds = model("리고 초코 시럽 585g 2개세트  (주)비앤씨인터내셔널")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 4   | 10.8025 | 29  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 50                    |
| 1.0   | 50                    |
| 2.0   | 50                    |
| 3.0   | 50                    |
| 4.0   | 50                    |
| 5.0   | 50                    |
| 6.0   | 50                    |
| 7.0   | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0159  | 1    | 0.4035        | -               |
| 0.7937  | 50   | 0.322         | -               |
| 1.5873  | 100  | 0.125         | -               |
| 2.3810  | 150  | 0.0315        | -               |
| 3.1746  | 200  | 0.0111        | -               |
| 3.9683  | 250  | 0.0005        | -               |
| 4.7619  | 300  | 0.0002        | -               |
| 5.5556  | 350  | 0.0001        | -               |
| 6.3492  | 400  | 0.0001        | -               |
| 7.1429  | 450  | 0.0001        | -               |
| 7.9365  | 500  | 0.0001        | -               |
| 8.7302  | 550  | 0.0001        | -               |
| 9.5238  | 600  | 0.0001        | -               |
| 10.3175 | 650  | 0.0001        | -               |
| 11.1111 | 700  | 0.0           | -               |
| 11.9048 | 750  | 0.0001        | -               |
| 12.6984 | 800  | 0.0           | -               |
| 13.4921 | 850  | 0.0           | -               |
| 14.2857 | 900  | 0.0           | -               |
| 15.0794 | 950  | 0.0           | -               |
| 15.8730 | 1000 | 0.0           | -               |
| 16.6667 | 1050 | 0.0           | -               |
| 17.4603 | 1100 | 0.0           | -               |
| 18.2540 | 1150 | 0.0001        | -               |
| 19.0476 | 1200 | 0.0           | -               |
| 19.8413 | 1250 | 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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