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---
base_model: mini1013/master_domain
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 이브로쉐 모링가 리프레시 헤어 식초 400ml 1개 옵션없음 주식회사 다올연구소
- text: Hair Identifier Spray for Face Shaving 2024 Skin Dermaplaning Moisturizing
    and Care Dermaplaner 2 PC 옵션없음 젠틀스토어
- text: 수앤 오리진 블랙 단백질샴푸700ml,4개 옵션없음 다부자
- text: 클로란 퀴닌 에델바이스 두피 세럼 100ml 옵션없음 스루치로 유한책임회사
- text: 이브로쉐 리프레쉬 헤어식초(모링가) 400ml 옵션없음 스루치로 유한책임회사
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: accuracy
      value: 0.6042402826855123
      name: Accuracy
---

# 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>'CHI 실크 인퓨전 12 Fl oz (관부가세포함) 옵션없음 제이글로벌컴퍼니'</li><li>'아모스 리페어 샤인 모이스트 에센스 100ml 옵션없음 티비'</li><li>'BAO H LAB Hair Loss Care Ampoule 바오에이치랩 탈모케어앰플 옵션없음 주식회사 바오젠'</li></ul>                                                   |
| 7.0   | <ul><li>'커리쉴 프레스티지 실키 3종 옵션없음 (주)커리쉴'</li><li>'미쟝센 퍼펙트 매직 스트레이트 샴푸&트리트먼트&세럼 3종 세트+트리트먼트 30ml 아모레퍼시픽'</li><li>'[르도암 공식]르도암 카멜리아 헤어 2종 세트(샴푸+트리트먼트) LEDOAM1935'</li></ul>                                                             |
| 0.0   | <ul><li>'실키드 검은콩 코팅 탈모펜슬™ / 머리숱앰플 두피앰플 산후탈모 서리태 비건 에센스 홈 1개 (1개월) 탈모펜슬™ 주식회사 팀오브라만차(Team of la mancha Corp.)'</li><li>'에버미라클 200ml EM 풀라무 토너 스칼프 토닉 8W98E7F225 옵션없음 파워몰'</li><li>'포티샤 모발강화 두피세럼 100ml/르네휘테르 옵션없음 롯데쇼핑(주)'</li></ul> |
| 4.0   | <ul><li>'[클렌징대전(클렌징밤 )] 로픈 바오밥 세라마이드LPP 프리미엄 헤어트리트먼트 베이비파우더향 1000g 옵션없음 (주)우신뷰티'</li><li>'허벌리스테 헤어 리페어세럼 150ml 1개 + 헤어 마스크 500ml - 1개 옵션없음 복슬강아지'</li><li>'[백화점 정품] 모로칸오일 오리지널 오일 트리트먼트 100ml 제3자 배송관련 개인정보활용에 동의함 버니버즈'</li></ul>  |
| 2.0   | <ul><li>'헤드앤숄더 시트러스 레몬 샴푸 750ml 옵션없음 포에이치제이'</li><li>'아렌 일진 산성샴푸펌컬러 1000ml 옵션없음 해문인터내셔널'</li><li>'물없이쓰는샴푸 물없이머리감는 입원준비물 노워시 옵션없음 해피2데이'</li></ul>                                                                                   |
| 5.0   | <ul><li>'바이오테닉스 홈케어 매직헬프 바이-페이즈 리컨디셔너 60ml 비너스 클리닉 옵션없음 주식회사 위즈온컴퍼니'</li><li>'[바이레도] 블랑쉬 헤어퍼퓸 75ml 화이트_F 푸치코리아 유한책임회사'</li><li>'바이레도 집시 워터 헤어퍼퓸 75ml 백화점 상품 옵션없음 코코스팜'</li></ul>                                                    |
| 1.0   | <ul><li>'케라시스린스 퍼퓸 체리블라썸 1000ml 옵션없음 땡그리나'</li><li>'[갤러리아] [비건 NEW] 진저 스캘프 케어 대용량 컨디셔너 400ML(한화갤러리아㈜ 광교점) 옵션없음 한화갤러리아(주)'</li><li>'케라시스 스위트 앤 플라워리 퍼퓸 린스 1L 옵션없음 해피쭈몰'</li></ul>                                                    |
| 3.0   | <ul><li>'모비88 아데노신 특허등록 탈모토닉 볼륨업 비듬 제거 옵션없음 달이커머스'</li><li>'힐텀 어성초 맥주효모 토닉 120ml 옵션없음 현스 마켓'</li><li>'닥터포헤어 폴리젠 토닉 120ml x 2개 두피 영양공급 탈모증상완화 영양제 코스트코 옵션없음 또또상회'</li></ul>                                                          |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.6042   |

## 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_bt12_test")
# Run inference
preds = model("수앤 오리진 블랙 단백질샴푸700ml,4개 옵션없음 다부자")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 4   | 9.25   | 21  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 12                    |
| 1.0   | 23                    |
| 2.0   | 19                    |
| 3.0   | 14                    |
| 4.0   | 18                    |
| 5.0   | 20                    |
| 6.0   | 28                    |
| 7.0   | 18                    |

### Training Hyperparameters
- batch_size: (512, 512)
- num_epochs: (50, 50)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 60
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch   | Step | Training Loss | Validation Loss |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0556  | 1    | 0.4865        | -               |
| 2.7778  | 50   | 0.3392        | -               |
| 5.5556  | 100  | 0.0584        | -               |
| 8.3333  | 150  | 0.0087        | -               |
| 11.1111 | 200  | 0.003         | -               |
| 13.8889 | 250  | 0.0002        | -               |
| 16.6667 | 300  | 0.0001        | -               |
| 19.4444 | 350  | 0.0001        | -               |
| 22.2222 | 400  | 0.0001        | -               |
| 25.0    | 450  | 0.0001        | -               |
| 27.7778 | 500  | 0.0001        | -               |
| 30.5556 | 550  | 0.0           | -               |
| 33.3333 | 600  | 0.0           | -               |
| 36.1111 | 650  | 0.0           | -               |
| 38.8889 | 700  | 0.0           | -               |
| 41.6667 | 750  | 0.0           | -               |
| 44.4444 | 800  | 0.0           | -               |
| 47.2222 | 850  | 0.0           | -               |
| 50.0    | 900  | 0.0           | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.3.1
- Transformers: 4.44.2
- PyTorch: 2.2.0a0+81ea7a4
- Datasets: 3.2.0
- Tokenizers: 0.19.1

## 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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