---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: '[즉시15%+중복20%] 예꼬맘 어린이 실크 칫솔 0.07mm 3개 예꼬맘실크칫솔화이트 1단계_예꼬맘실크칫솔핑크 2단계_예꼬맘실크칫솔옐로우
    1단계 출산/육아 > 유아세제 > 유아세탁비누'
- text: 2023 에브리케어 블랙프라이데이 12. 주방세제 500g 출산/육아 > 유아세제 > 유아세탁세제
- text: 마이비 피부에순한 유아섬유유연제 (리필 1600ml)  출산/육아 > 유아세제 > 유아세탁비누
- text: 베르블랑 중성 아기세제 1L X 3개 (프리미엄 가루세제 구연산 1kg) 머스크향[VB-LM3]_프리미엄 가루세제 구연산 1kg[VB-CA1]
    출산/육아 > 유아세제 > 유아세탁세제
- text: 비브라이트 어린이 LED 타이머 유아칫솔 양치컵홀더3P세트 핑크냐옹 출산/육아 > 유아세제 > 유아세탁비누
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:** 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                                                                                                                                                                                                                                                                                                            |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 4.0   | <ul><li>'비앤비 섬유유연제 베르가못 캡리필 1800ml X 6개  출산/육아 > 유아세제 > 혼합세트'</li><li>'베비언스 아기세제 핑크퐁 베이비 아기섬유유연제 유아세탁세제 유아세제 섬유유연제  출산/육아 > 유아세제 > 혼합세트'</li><li>'레드루트 유아 아기 세탁세제1L+유연제1L 향 선택 머스크플로랄_바닐라코튼 출산/육아 > 유아세제 > 혼합세트'</li></ul>                                                                                          |
| 2.0   | <ul><li>'생활용품세탁세재욕실청소 베르블랑 유아 섬유 유연제 그린플로럴향 1000ml, 1개 샹활용품욕실청소세탁세재 생활용품욕실청소세탁세재 생활용품 1000ml × 3개 출산/육아 > 유아세제 > 유아유연제'</li><li>'레드루트 건조기시트 섬유유연제 50매 향선택 건조기시트50매_스위트 출산/육아 > 유아세제 > 유아유연제'</li><li>'비앤비 유아 아기 신생아 섬유유연제 1800ml 3팩 리필 베이비 유연제 액상형 섬유린스 세제/유연제_09.유연제베르가못1800ml리필×4 출산/육아 > 유아세제 > 유아유연제'</li></ul> |
| 3.0   | <ul><li>'마더케이 디아 산소계 표백제 1kg (무향)  출산/육아 > 유아세제 > 유아표백제/얼룩제거제'</li><li>'비앤비 얼룩제거제 500ml 옷얼룩제거 유아옷 얼룩제거 천연성분 함유 젖병세정제_거품 450ml 용기 출산/육아 > 유아세제 > 유아표백제/얼룩제거제'</li><li>'마이비 얼룩제거제 330ml + 리필 300ml x 3개  출산/육아 > 유아세제 > 유아표백제/얼룩제거제'</li></ul>                                                                        |
| 0.0   | <ul><li>'아이앤어스 독일더마 프리미엄 캡슐형 세탁세제 30개입 x4팩 아이앤어스 독일더마 프리미엄 캡슐형 세탁세제 출산/육아 > 유아세제 > 유아세탁비누'</li><li>'러블리앙즈 유아마스크 30매 어린이 3D 새부리형 초소형 소형 4 8세_M9 왕관곰 4 8세 출산/육아 > 유아세제 > 유아세탁비누'</li><li>'네이쳐러브메레 유연제, 리필, 체리블러썸향, 1300ml, 4개 체리블러썸 유연제 4개 오리지널 세제 4개 출산/육아 > 유아세제 > 유아세탁비누'</li></ul>                                |
| 1.0   | <ul><li>'레드루트 유아 섬유유연제 세탁세제 1L 3개세트 바닐라코튼 세탁세제 _ 머스크_세탁세제 _ 스위트_유연제 _ 바닐라 출산/육아 > 유아세제 > 유아세탁세제'</li><li>'베비언스 핑크퐁 베이비 세탁세제 리필 2.2L  출산/육아 > 유아세제 > 유아세탁세제'</li><li>'[3개] 아이너바움 대용량 세탁세제 3개세트 무향 / 유아 아기 신생아 비건인증 세제 3.비건인증 안심 세제 3종(세탁2+섬유1)_세탁세제(네이처플라워 2개)_섬유유연제(스윗선데이 1개) 출산/육아 > 유아세제 > 유아세탁세제'</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_bc23")
# Run inference
preds = model("마이비 피부에순한 유아섬유유연제 (리필 1600ml)  출산/육아 > 유아세제 > 유아세탁비누")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 8   | 15.3086 | 31  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 70                    |
| 1.0   | 70                    |
| 2.0   | 70                    |
| 3.0   | 70                    |
| 4.0   | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0145  | 1    | 0.4811        | -               |
| 0.7246  | 50   | 0.4993        | -               |
| 1.4493  | 100  | 0.4843        | -               |
| 2.1739  | 150  | 0.276         | -               |
| 2.8986  | 200  | 0.0128        | -               |
| 3.6232  | 250  | 0.0           | -               |
| 4.3478  | 300  | 0.0           | -               |
| 5.0725  | 350  | 0.0           | -               |
| 5.7971  | 400  | 0.0           | -               |
| 6.5217  | 450  | 0.0           | -               |
| 7.2464  | 500  | 0.0           | -               |
| 7.9710  | 550  | 0.0           | -               |
| 8.6957  | 600  | 0.0           | -               |
| 9.4203  | 650  | 0.0           | -               |
| 10.1449 | 700  | 0.0           | -               |
| 10.8696 | 750  | 0.0           | -               |
| 11.5942 | 800  | 0.0           | -               |
| 12.3188 | 850  | 0.0           | -               |
| 13.0435 | 900  | 0.0           | -               |
| 13.7681 | 950  | 0.0           | -               |
| 14.4928 | 1000 | 0.0           | -               |
| 15.2174 | 1050 | 0.0           | -               |
| 15.9420 | 1100 | 0.0           | -               |
| 16.6667 | 1150 | 0.0           | -               |
| 17.3913 | 1200 | 0.0           | -               |
| 18.1159 | 1250 | 0.0           | -               |
| 18.8406 | 1300 | 0.0           | -               |
| 19.5652 | 1350 | 0.0           | -               |
| 20.2899 | 1400 | 0.0           | -               |
| 21.0145 | 1450 | 0.0           | -               |
| 21.7391 | 1500 | 0.0           | -               |
| 22.4638 | 1550 | 0.0           | -               |
| 23.1884 | 1600 | 0.0           | -               |
| 23.9130 | 1650 | 0.0           | -               |
| 24.6377 | 1700 | 0.0           | -               |
| 25.3623 | 1750 | 0.0           | -               |
| 26.0870 | 1800 | 0.0           | -               |
| 26.8116 | 1850 | 0.0           | -               |
| 27.5362 | 1900 | 0.0           | -               |
| 28.2609 | 1950 | 0.0           | -               |
| 28.9855 | 2000 | 0.0           | -               |
| 29.7101 | 2050 | 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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