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:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 고린GORIN 버튼식 자전거 링자물쇠 도난방지 일본 발매 GR520-SL 스포츠/레저>자전거>자전거용품>자물쇠
- text: Race Face 좁은 와이드 신치 체인링 시마노 12단 스피드 30t 스포츠/레저>자전거>자전거부품>체인
- text: 폭스레이싱 프리 저지 롱프리 팬츠 세트 179 195M 자전거의류 라이딩복 싸이클상의 바지 7부소매 스포츠/레저>자전거>자전거의류/잡화>상하세트
- text: 브레이크호스 브레이크 유압 케이블 오일 스포츠/레저>자전거>자전거부품>브레이크
- text: 사일런스 반팔져지 에어로핏 스포츠/레저>자전거>자전거의류/잡화>상의
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: 1.0
      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:** 4 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                                                                                                                                                                                                                     |
|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1.0   | <ul><li>'시마노 SHIMANO 크랭크 세트 12s 32T FC-M6120-1 EFCM61201EXA2 스포츠/레저>자전거>자전거부품>변속기'</li><li>'트레벨로 접이식 폴딩 실내자전거거치대 스포츠/레저>자전거>자전거부품>스탠드'</li><li>'MTB 포크 자전거 서스펜션 앞 26 27 5 충격흡수 Fork 쇼바 합금 스포츠/레저>자전거>자전거부품>프레임/포크'</li></ul> |
| 2.0   | <ul><li>'파크툴 106 워크 트레이 정비대 액세서리 스포츠/레저>자전거>자전거용품>공구'</li><li>'비엠웍스 로드 자전거 물통 컨투어 750 32032038 스포츠/레저>자전거>자전거용품>케이지'</li><li>'RBRL 자갈 자전거 윙 플랫 핸들 로드 펜더 퀵릴리즈 700c 머드가드 스포츠/레저>자전거>자전거용품>흙받이'</li></ul>                       |
| 0.0   | <ul><li>'엠비에스코퍼레이션 엘파마 벤토르 V2000 MTB 자전거 2022년 스포츠/레저>자전거>자전거/MTB>MTB'</li><li>'QUAX 온리원 외발자전거 스포츠/레저>자전거>자전거/MTB>특수자전거'</li><li>'ATECX 컴포트 2700D 유사MTB 2023년 스포츠/레저>자전거>자전거/MTB>유사MTB'</li></ul>                            |
| 3.0   | <ul><li>'라이딩 백팩 대용량 방수 오토바이 헬멧 가방 바이크 스포츠/레저>자전거>자전거의류/잡화>배낭'</li><li>'Castelli 뉴 카스텔리 아리아 여성 방풍 사이클링 바람 조끼 DARK 스포츠/레저>자전거>자전거의류/잡화>상의'</li><li>'ENDURANCE 엔듀런스 지구력 저스틴 - 조끼 322109 스포츠/레저>자전거>자전거의류/잡화>상의'</li></ul>       |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 1.0      |

## 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_sl25")
# Run inference
preds = model("사일런스 반팔져지 에어로핏 스포츠/레저>자전거>자전거의류/잡화>상의")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 4   | 8.5714 | 21  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 70                    |
| 1.0   | 70                    |
| 2.0   | 70                    |
| 3.0   | 70                    |

### Training Hyperparameters
- batch_size: (256, 256)
- num_epochs: (30, 30)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 50
- 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0182  | 1    | 0.4882        | -               |
| 0.9091  | 50   | 0.4972        | -               |
| 1.8182  | 100  | 0.3608        | -               |
| 2.7273  | 150  | 0.0243        | -               |
| 3.6364  | 200  | 0.0           | -               |
| 4.5455  | 250  | 0.0           | -               |
| 5.4545  | 300  | 0.0           | -               |
| 6.3636  | 350  | 0.0           | -               |
| 7.2727  | 400  | 0.0           | -               |
| 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  | 0.0           | -               |
| 18.1818 | 1000 | 0.0           | -               |
| 19.0909 | 1050 | 0.0           | -               |
| 20.0    | 1100 | 0.0           | -               |
| 20.9091 | 1150 | 0.0           | -               |
| 21.8182 | 1200 | 0.0           | -               |
| 22.7273 | 1250 | 0.0           | -               |
| 23.6364 | 1300 | 0.0           | -               |
| 24.5455 | 1350 | 0.0           | -               |
| 25.4545 | 1400 | 0.0           | -               |
| 26.3636 | 1450 | 0.0           | -               |
| 27.2727 | 1500 | 0.0           | -               |
| 28.1818 | 1550 | 0.0           | -               |
| 29.0909 | 1600 | 0.0           | -               |
| 30.0    | 1650 | 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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