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cdabd80

---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 템퍼 컴포트 에어 베개 소프트 NEW 가구/인테리어>베개>메모리폼베개
- text: 메이슬립 메모리폼 경추 베개 거북목 일자목 목이편한 숙면 편하베개 가구/인테리어>베개>계절베개
- text: 다니카 다니카 프리미엄 3D 경추 메모리폼 베개 캠핑베개 휴대용 배개 크림 언니 베게 가구/인테리어>베개>메모리폼베개
- text: 스트라이프 자카드 술 베개 커버 장식 코튼 리넨 원사 염색 쿠션 북유럽 홈 케이스 가구/인테리어>베개>베개커버세트
- text: 아망떼 뉴데이즈 베개커버 40x60 2장 가구/인테리어>베개>베개커버
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: mini1013/master_domain
---

# 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 -->
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### 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                                                                                                                                                                                                                |
|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 3.0   | <ul><li>'에이트룸 뱀부 프릴 시어서커 베개커버 50X70 가구/인테리어>베개>베개커버'</li><li>'파르페by알레르망 휴비스 듀라론 냉감 베개커버 방석 바디필로우 쇼파패드 냉감패드 쿨매트 4인 75x240 가구/인테리어>베개>베개커버'</li><li>'마틸라 NEW컬러 미드센추리 빈티지맨션 60수 고밀도순면 베개커버-11컬러 가구/인테리어>베개>베개커버'</li></ul> |
| 4.0   | <ul><li>'마스터유닛2 - 소프트 가구/인테리어>베개>베개커버세트'</li><li>'제이앤우 기절베개 홈랩 오리지널 기절베개 속통 세트구성 파인애플 베개 릴렉스 필로우 베개커버 통세탁 진드기차단 가구/인테리어>베개>베개커버세트'</li><li>'제이앤우 기절베개 오리지널 기절베개 세트구성 호텔식 베개 통세탁가능 가구/인테리어>베개>베개커버세트'</li></ul>          |
| 0.0   | <ul><li>'즐잠 메밀베개 편백나무 경추베개 목디스크 거북목 일자목 목침 가구/인테리어>베개>계절베개'</li><li>'조은잠 특허받은 허리베개 요추베개 다용도 교정베개 가구/인테리어>베개>계절베개'</li><li>'메이슬립 메모리폼 경추 베개 거북목 일자목 목이편한 숙면 편하베개 가구/인테리어>베개>계절베개'</li></ul>                              |
| 2.0   | <ul><li>'스패로우 스프링 필로우 메모리폼 베개 가구/인테리어>베개>메모리폼베개'</li><li>'템퍼 템퍼베개 오리지날 베개 S 가구/인테리어>베개>메모리폼베개'</li><li>'템퍼 밀레니엄 베개 SmartCool 가구/인테리어>베개>메모리폼베개'</li></ul>                                                               |
| 1.0   | <ul><li>'3D와플 경추 견인 베개 메모리폼 라텍스 베게 배게 가구/인테리어>베개>라텍스베개'</li><li>'일자목 안락 낮은 베개 거북목 목 보호 라텍스 숙면 -6cm 진드기 방지 베갯잇 가구/인테리어>베개>라텍스베개'</li><li>'성형 베개 뒤척임 방지 수술 후 코 양악 리프팅 가슴 눈 주름 윤곽 관리 베개 13종 가구/인테리어>베개>라텍스베개'</li></ul>    |

## 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_fi2")
# Run inference
preds = model("템퍼 컴포트 에어 베개 소프트 NEW 가구/인테리어>베개>메모리폼베개")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 8.7293 | 17  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 37                    |
| 1.0   | 22                    |
| 2.0   | 23                    |
| 3.0   | 34                    |
| 4.0   | 17                    |

### 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0385  | 1    | 0.4798        | -               |
| 1.9231  | 50   | 0.2701        | -               |
| 3.8462  | 100  | 0.0001        | -               |
| 5.7692  | 150  | 0.0001        | -               |
| 7.6923  | 200  | 0.0           | -               |
| 9.6154  | 250  | 0.0           | -               |
| 11.5385 | 300  | 0.0           | -               |
| 13.4615 | 350  | 0.0           | -               |
| 15.3846 | 400  | 0.0           | -               |
| 17.3077 | 450  | 0.0           | -               |
| 19.2308 | 500  | 0.0           | -               |
| 21.1538 | 550  | 0.0           | -               |
| 23.0769 | 600  | 0.0           | -               |
| 25.0    | 650  | 0.0           | -               |
| 26.9231 | 700  | 0.0           | -               |
| 28.8462 | 750  | 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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