---
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: 땀흡수 스포츠 운동 면 머리 헤어밴드 여자아이 고등학생 여성 검정 드렉온미
- text: 니켄 접이형 다리털제거기 1p 숱제거기 다리털면도 옵션없음 제이에이치코리아
- text: 관리 눈썹면도기 면도 미용 니켄 일자형 눈썹칼 옵션없음 프렌드리빙
- text: 천사의 웨딩드레스는 빠르게 승인받을 수 있는 로즈 레드 신부 웨 10001N548703 중_로즈 레드 선배
- text: 립브러쉬 실리콘 립스머지 휴대용 투명 미리
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: 0.6375838926174496
      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                                                                                                                                                                                                 |
|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 2.0   | <ul><li>'이니스프리 샤워 볼 1ea 이니스프리'</li><li>'3컬러 양면 귀이개 택1 TW51DC3F0 블랙 블루 블루 트리플도매'</li><li>'프리미엄 실리콘 니플 가리개 여성 니플 패치 원형 알스상회'</li></ul>                                                                     |
| 0.0   | <ul><li>'카페인 커피 샴푸바만들기 교육용 수제비누 키트 DIY 자원순환 업사이클링 옵션없음 처음(CHOEUM)'</li><li>'플로럴워터 - 로즈마리워터 1 리터 옵션없음 주식회사 월터엔터프라이즈'</li><li>'봄봄솝 바다 비누 만들기 DIY 조개 집콕 미술 (6개 완성, 조개몰드포함) 옵션없음 봄상회'</li></ul>              |
| 6.0   | <ul><li>'고양이귀 세안 헤어밴드 5p세트 KD-8679 목욕용 세면 샤워용 극세사 옵션없음 초이스리테일 5'</li><li>'긴머리 샤워캡 PEVA 방수 도트 헤어캡 핑크도트 허승호'</li><li>'편리한 찍찍이타입 머리밴드 스카이 옵션없음 와이엠테크(YM tech)'</li></ul>                                    |
| 5.0   | <ul><li>'에이브 면분첩 - 중형 옵션없음 하민하이'</li><li>'마스크 2 TYPE NEW갸름마스크턱볼살용 얼굴 턱볼살 옵션없음 유남상사'</li><li>'보정웨어 TYPE 턱볼살땡 몸매관리 2 마스크 얼굴 옵션없음 최상용'</li></ul>                                                            |
| 7.0   | <ul><li>'가루 파우더 케이스 30g 노세범 땀띠 파우더 소분 공병 (스푼 ) 30g 선데이베리베스트'</li><li>'면봉보관함 화장솜 케이스 디스펜서 통 옵션없음 홍스지니몰'</li><li>'실리콘공병 보틀 고리형 4종세트 추가금X 그루비스윔 수영장 여행 헬스장 캠핑용 소분용기 옵션없음 스퀘어오브에이치'</li></ul>                |
| 3.0   | <ul><li>'눈썹 족집게 오렌지 C 1p 청결용품 눈관리 핀셋 옵션없음 비즈파크'</li><li>'텐웨이브 쌍꺼풀테이프 레이스 티안나는 누드쌍테 단면쌍테 쌍커풀테이프 옵션없음 텐웨이브'</li><li>'1+1+1 할인 일자형 눈썹정리 눈썹칼 3P 옵션없음 버닝365마켓'</li></ul>                                      |
| 4.0   | <ul><li>'이레즈미 타투스티커 초대형 (여성용) 긴팔 옵션없음 알렉산더(ALEXANDER)'</li><li>'미니 타투 스티커 헤나 도안 형광 야광 HC-016 컬러타투 CC시리즈_CC-028 블루밍마켓'</li><li>'2주지속 리얼 문신 팔손가락 타투스티커 티안나는 반영구 방수 헤나 문신 나비 세트 A6 ( 2장세트 ) 에테르넬'</li></ul> |
| 1.0   | <ul><li>'립펜슬 실버 고급립솔 화장브러쉬 옵션없음 엔에이티글로벌'</li><li>'아이라인붓 애교살브러쉬 눈썹브러쉬 1100-5 아이라인브러시 옵션없음 동묘야시장'</li><li>'아이브로우브러쉬 8pcs Cardcaptor 세트 파운데이션 섀도우 브로우 Pincel 8pcs_CHINA 드림비정선'</li></ul>                    |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.6376   |

## 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_bt5_test")
# Run inference
preds = model("립브러쉬 실리콘 립스머지 휴대용 투명 미리")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 3   | 10.0538 | 20  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 12                    |
| 1.0   | 12                    |
| 2.0   | 12                    |
| 3.0   | 19                    |
| 4.0   | 20                    |
| 5.0   | 27                    |
| 6.0   | 13                    |
| 7.0   | 15                    |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0625 | 1    | 0.4921        | -               |
| 3.125  | 50   | 0.2813        | -               |
| 6.25   | 100  | 0.0272        | -               |
| 9.375  | 150  | 0.0167        | -               |
| 12.5   | 200  | 0.002         | -               |
| 15.625 | 250  | 0.0001        | -               |
| 18.75  | 300  | 0.0001        | -               |
| 21.875 | 350  | 0.0001        | -               |
| 25.0   | 400  | 0.0001        | -               |
| 28.125 | 450  | 0.0001        | -               |
| 31.25  | 500  | 0.0001        | -               |
| 34.375 | 550  | 0.0001        | -               |
| 37.5   | 600  | 0.0001        | -               |
| 40.625 | 650  | 0.0001        | -               |
| 43.75  | 700  | 0.0001        | -               |
| 46.875 | 750  | 0.0001        | -               |
| 50.0   | 800  | 0.0001        | -               |

### 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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