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---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 벨라이프 마리포사 테카포 실리콘 테이블매트 가구/인테리어>홈데코>주방데코>식탁매트
- text: 에코벨 숨쉬는 분리형 소파 쿠션 대형 침대 독서 팔걸이 가구/인테리어>홈데코>쿠션/방석>일반쿠션
- text: 샤이닝홈 리버블 메리산타 크리스마스 쿠션 솜포함 45x45 가구/인테리어>홈데코>쿠션/방석>일반쿠션
- text: 오븐장갑 모자 13컬러 주방장갑 냄비 손잡이 가구/인테리어>홈데코>주방데코>주방장갑
- text: 옥스포드 순면 무지 솔리드 순면 의자 학생 카페 벤치 방석커버 40 45 50 60 40x40 백아이보리 가구/인테리어>홈데코>쿠션/방석>일반방석
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: mini1013/master_domain
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:** 3 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>'야외 베개 삼각 봄 꽃 간단한 방수 장식 소파 커버 쿠션 가구/인테리어>홈데코>쿠션/방석>쿠션/방석커버세트'</li><li>'컬러 쿠션 커버 단색 린넨 소파 자동차 장식 베개 간단한 가구/인테리어>홈데코>쿠션/방석>방석커버'</li><li>'코멧 여름 필수템 실리콘 방석 커버 세트 대형 가구/인테리어>홈데코>쿠션/방석>방석커버'</li></ul>                       |
| 0.0   | <ul><li>'두툼한 내열 뚝배기 땡땡이리본 주방장갑 보호장갑 주방용장갑 키친툴 가구/인테리어>홈데코>주방데코>주방장갑'</li><li>'테이블매트 방수 커피색 북유럽 식탁매트 식탁보 가구/인테리어>홈데코>주방데코>식탁매트'</li><li>'인터그레이스 앳홈 기름때방지 주방아트보드 고래의 꿈 일러스트 디자인 3M 부착형 895x580mm 가구/인테리어>홈데코>주방데코>기타주방데코'</li></ul> |
| 1.0   | <ul><li>'실외기 덮개 가림막 중 에어컨 실외기커버 가구/인테리어>홈데코>커버류>에어컨커버'</li><li>'벽걸이 에어컨커버 캐릭터 스판 덮개 에어컨 보관 가구/인테리어>홈데코>커버류>에어컨커버'</li><li>'피아노 커버 덮개 세트 스툴 의자 학원 건반 천 패브릭 가구/인테리어>홈데코>커버류>피아노커버'</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_fi16")
# Run inference
preds = model("벨라이프 마리포사 테카포 실리콘 테이블매트 가구/인테리어>홈데코>주방데코>식탁매트")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 9.2857 | 19  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 70                    |
| 1.0   | 70                    |
| 2.0   | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0238  | 1    | 0.4981        | -               |
| 1.1905  | 50   | 0.4801        | -               |
| 2.3810  | 100  | 0.0492        | -               |
| 3.5714  | 150  | 0.0           | -               |
| 4.7619  | 200  | 0.0           | -               |
| 5.9524  | 250  | 0.0           | -               |
| 7.1429  | 300  | 0.0           | -               |
| 8.3333  | 350  | 0.0           | -               |
| 9.5238  | 400  | 0.0           | -               |
| 10.7143 | 450  | 0.0           | -               |
| 11.9048 | 500  | 0.0           | -               |
| 13.0952 | 550  | 0.0           | -               |
| 14.2857 | 600  | 0.0           | -               |
| 15.4762 | 650  | 0.0           | -               |
| 16.6667 | 700  | 0.0           | -               |
| 17.8571 | 750  | 0.0           | -               |
| 19.0476 | 800  | 0.0           | -               |
| 20.2381 | 850  | 0.0           | -               |
| 21.4286 | 900  | 0.0           | -               |
| 22.6190 | 950  | 0.0           | -               |
| 23.8095 | 1000 | 0.0           | -               |
| 25.0    | 1050 | 0.0           | -               |
| 26.1905 | 1100 | 0.0           | -               |
| 27.3810 | 1150 | 0.0           | -               |
| 28.5714 | 1200 | 0.0           | -               |
| 29.7619 | 1250 | 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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