setfit safetensors roberta sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:mini1013/master_domain base_model:finetune:mini1013/master_domain model-index region:us
---
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: 가스코 가죽전용염색약 소파 카시트 스니커즈 33색상 100ml 다크브라운 주식회사가스코
- text: 레인슈즈 장화 방수 부츠 수중작업 신발보호 고무 미끄럼방지 여성용 H_M 34-36 지에스
- text: 가스코 가죽전용염색약 도구 풀세트 가죽옷 100ml 브라운_무광 주식회사 가스코
- text: 엑스솔 에어슬림 인솔 기능성 신발 깔창 245mm 주식회사 영창에코
- text: 깁스 양말 싸개 발 보호 보온 방한 편한 이쁜 롱 부츠형 여성 방수커버 샤워 팔 남성용 플러시 슬리브/두꺼운 버전  높이 35_45
    핑크고릴라
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: 0.9254610935283204
      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:** 7 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>'등산화끈 1+1 통끈_라인네이비 신세계몰'</li><li>'등산화끈 1+1 트위스트_브라운 신세계몰'</li><li>'몽벨 슈레이스 플랫 4MM YELLOW JBSXXUZZ105 신발끈 평끈 등산화끈 140 (주)코어밸류'</li></ul>                                                                                                      |
| 2.0   | <ul><li>'고급 강남스타힐 구두굽/소음방지/충격완화/하이힐굽 블랙_DD-107 슈미즈'</li><li>'발 뒤꿈치 패드 쿠션 신발 구두 운동화 사이즈 클때 줄이기 패드 6-피넛_아이보리화이트_One Size(2P) 저스트에잇'</li><li>'고급 강남스타힐 구두굽/소음방지/충격완화/하이힐굽 블랙_DD-092 슈미즈'</li></ul>                                                      |
| 5.0   | <ul><li>'[웰럽] 시그니처 깔창 아치 운동화 등산화 군대 군인 군화 안전화 평발 기능성 키높이 [0008]그린 M(255 270) CJONSTYLE'</li><li>'[롯데백화점]에코(슈즈) 컴포트 에브리데이 인솔 멘즈 9059029-00101 블랙_EU 39 롯데백화점_'</li><li>'등산화 깔창 기능성 운동화 특수 스포츠 신발 키높이 골프화 XL275-295 마켓퀸즈'</li></ul>                    |
| 0.0   | <ul><li>'[현대백화점]금강제화 랜드로바 SHOSC0150SAM 휴대용 미니 구두헤라 [00001] 휴대용 구두칼 (주)현대홈쇼핑'</li><li>'에드가 체크 소가죽 휴대용 슈혼 navy 000 (주)트라이본즈'</li><li>'[금강제화](광주신세계) 콜렉션 휴대용 슈혼 스틸 미니 구두 헤라 N8MKA150/SHOSC0150SAM 10.5cm 신세계백화점'</li></ul>                              |
| 4.0   | <ul><li>'비오는날 남성 여성 1회용비닐덧신 S M L 비올때신발 여름필수품 신발우비 색상_레인신발커버 투명블루M 오픈리빙'</li><li>'비올때 이색적인 여성용 싱글 슈즈 가죽 신발 여성 패션 레인신발커버 멋스러운코디 13_39 스톰브랜상범'</li><li>'투명 슈즈 패션 워터 장마 여성장화 미끄럼방지 학생 XXL(43-45 적합)_블루-하이 [미끄럼방지창x2년 품질] 구룡글로벌'</li></ul>               |
| 1.0   | <ul><li>'곰돌이 블랙 검정하트 화이트 남자 성인 커플 지비추 자비츠 심플 파츠 클로그 참 장식신발 A set (블랙-화이트) 뉴지(NYUZY)'</li><li>'슈팁 금속팁 메탈락팁 듀브레 메탈밴드 악어클립 메탈고정핀 플라스틱고정핀 골드슈팁 황동슈팁 실버슈팁 금속슈팁 굵은골드(4개) 슈레이스'</li><li>'(SAPHIR) 사피르 레노베이팅 컬러 재생크림 / 가죽 염색제 리노베이팅 미디엄브라운 제이엠컴퍼니'</li></ul> |
| 3.0   | <ul><li>'STRATTON 남성용 삼나무 슈트리- 미국산, m / 9 - 10  알쓰리컴퍼니'</li><li>'발볼 여자 신발 남자 제골기 발등 여성하이힐 발등 코코나라'</li><li>'슈샤이너 전기 금속제골기 경첩타입 전문가용 레지가다 업소용 여성용 js9997'</li></ul>                                                                                   |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.9255 |

## 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_ac7")
# Run inference
preds = model("엑스솔 에어슬림 인솔 기능성 신발 깔창 245mm 주식회사 영창에코")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 3   | 10.4257 | 27  |

| 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                    |

### 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0182  | 1    | 0.3761        | -               |
| 0.9091  | 50   | 0.2291        | -               |
| 1.8182  | 100  | 0.033         | -               |
| 2.7273  | 150  | 0.018         | -               |
| 3.6364  | 200  | 0.0001        | -               |
| 4.5455  | 250  | 0.0001        | -               |
| 5.4545  | 300  | 0.0001        | -               |
| 6.3636  | 350  | 0.0001        | -               |
| 7.2727  | 400  | 0.0001        | -               |
| 8.1818  | 450  | 0.0           | -               |
| 9.0909  | 500  | 0.0           | -               |
| 10.0    | 550  | 0.0           | -               |
| 10.9091 | 600  | 0.0           | -               |
| 11.8182 | 650  | 0.0           | -               |
| 12.7273 | 700  | 0.0           | -               |
| 13.6364 | 750  | 0.0           | -               |
| 14.5455 | 800  | 0.0           | -               |
| 15.4545 | 850  | 0.0           | -               |
| 16.3636 | 900  | 0.0           | -               |
| 17.2727 | 950  | 0.0001        | -               |
| 18.1818 | 1000 | 0.0           | -               |
| 19.0909 | 1050 | 0.0           | -               |
| 20.0    | 1100 | 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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