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: 세이코 SBTR SBTR011 전용 힐링쉴드 시계보호필름 기스방지 유리보호필름 31평면 스타샵
- text: 시계줄 교체공구 스프링툴바/메탈,가죽밴드 변경도구/시계줄질도구 스프링바툴 멀티형 올리브tree
- text: 오메가호환 시계줄 스트랩 가죽 시계 체인 12 OMJ-브라운 화이트 라인 + 실버_20mm 더블드래곤(Double dragon)
- text: Uhgbsd 가죽 스트랩 VC 바쉐론 콘스탄틴 시계 호환 남성 액세서리 19mm 20mm 22mm 1_10 Black Gold Fold
    Bk 시구왕씨
- text: 디젤 DZ4316 DZ7395 7305 4209 4215 용 스테인레스 스틸 시계 호환용 남성용 메탈 솔리드 밴드 24mm 30mm
    04 B Black_05 30mm 아이스박스(ICEBOX)
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: 0.5793723141033988
      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:** 5 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                                                                                                                                                                                                                                                                  |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0.0   | <ul><li>'카시오 DW5600 시계 호환 16mm 러버 워치 밴드 실리콘 스트랩 우레탄 시계줄 옐로우 블랙 A_16mm 로움'</li><li>'갤럭시핏2 스트랩 실리콘 밴드 민트 보미헤안랩소디'</li><li>'로이드 어썸픽 소형 메쉬밴드 (2종 택 1) LL2B19611X LL2B19611XMG 로즈골드 세컨드플랜'</li></ul>                                                                           |
| 3.0   | <ul><li>'BOBO BIRD 네이비 블루 커플 손목 시계 연인 나무 쿼츠 맞춤형 각인 최고 럭셔리 브랜드 여성용 2.Paper Box 2 Woman 아더월드'</li><li>'캐주얼남녀손목시계 남자시계 폭발적인 벨트 테리어 시계 유럽 및 미국 시계선물 여자시계 Grey 리마113'</li><li>'남녀 커플 시계 SCRRJU 스테인레스 스틸 밴드 방수 연인 Se 패션 캐주얼 손목 선물 09 9 홀릭스'</li></ul>                           |
| 4.0   | <ul><li>'[프레드릭콘스탄트](신세계본점) FC-330MC4P6 클래식 문페이즈  주식회사 에스에스지닷컴'</li><li>'[다양한선물]순토 코어 올블랙 레귤러블랙 코어블랙레드 순토5 WHR 모음 시리즈 선택01.SS014279010 순토코어올블랙 스타샵'</li><li>'헬스공부타이머 집중공부타이머 요리 낮잠 여가 시간관리 알람 큐브 SW9EF763 15-60분 화이트 현대몰'</li></ul>                                        |
| 2.0   | <ul><li>'SUNOEL 3기압 5기압 방수 어린이 초등학생 전자 손목시계 모음  SUNOEL'</li><li>'손목시계쇼핑몰 아동용손목시계(16-5A) 손목시계대량  기프트한국'</li><li>'어린이 손목시계 초등학생 시계 키즈 전자시계 유아 스마트워치 남아 여아  제이에이취'</li></ul>                                                                                                 |
| 1.0   | <ul><li>'제작 빈 핀 버튼 메이커 부품 기계 용품 세트 25mm 32mm 37mm 44mm 50mm 56mm 58mm 50 개 [1]50sets_@#@[7]58mm 캐롤스하우스'</li><li>'무소음 무브먼트 시계 부품 모터 바늘 공예 DIY 선택D시계판_거북이 제이릴'</li><li>'시계공구 기타 야마하 YZF R125 R 125 YZFR125 20082013 바이크 오토바이 핸드가드 실드 핸드 가드 보호대 앞유리 07 Green 유비즈엘'</li></ul> |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.5794 |

## 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_ac6")
# Run inference
preds = model("세이코 SBTR SBTR011 전용 힐링쉴드 시계보호필름 기스방지 유리보호필름 31평면 스타샵")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 3   | 10.9107 | 22  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 50                    |
| 1.0   | 50                    |
| 2.0   | 24                    |
| 3.0   | 50                    |
| 4.0   | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0286  | 1    | 0.3696        | -               |
| 1.4286  | 50   | 0.1249        | -               |
| 2.8571  | 100  | 0.0114        | -               |
| 4.2857  | 150  | 0.0001        | -               |
| 5.7143  | 200  | 0.0001        | -               |
| 7.1429  | 250  | 0.0001        | -               |
| 8.5714  | 300  | 0.0001        | -               |
| 10.0    | 350  | 0.0001        | -               |
| 11.4286 | 400  | 0.0           | -               |
| 12.8571 | 450  | 0.0001        | -               |
| 14.2857 | 500  | 0.0           | -               |
| 15.7143 | 550  | 0.0           | -               |
| 17.1429 | 600  | 0.0           | -               |
| 18.5714 | 650  | 0.0           | -               |
| 20.0    | 700  | 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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