---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 마더케이 리필형 롱핸들 스펀지 실리콘 젖병솔 젖꼭지솔 세트 3.리필형 스펀지 젖꼭지솔 세트_베이지 출산/육아 > 소독/살균용품 > 젖병솔
- text: 비앤비 젖병세정제 거품형 용기 450ml B.BB젖병세제 거품형 용기450mlx1개 출산/육아 > 소독/살균용품 > 젖병세정제
- text: 마더케이 리필형 롱핸들스펀지솔 세척용품세트 (젖병건조대+리필브러쉬+젖병집게) 네이비_1.리필형젖병솔세트_초코브라운 출산/육아 > 소독/살균용품
    > 젖병건조대
- text: 다용도 플라스틱 물병 텀블러 건조대 보틀 건조대  출산/육아 > 소독/살균용품 > 젖병건조대
- text: 비앤비 유아 아기 젖병세정제 거품형 용기  출산/육아 > 소독/살균용품 > 젖병세정제
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: 0.9978046103183315
      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:** 7 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>'아기 젖병 식기 주방 세정제 유아 신생아 중성세제 거품형(리필400ml) 출산/육아 > 소독/살균용품 > 젖병세정제'</li><li>'마더케이 디아 아기 유아 이유식기 세정제 500ml (액상형/무향) 이유식기세정제500ml(무향)_이유식기세정제500ml(무향) 출산/육아 > 소독/살균용품 > 젖병세정제'</li><li>'비앤비 젖병세정제 거품형 리필 400ml x 4개  출산/육아 > 소독/살균용품 > 젖병세정제'</li></ul>             |
| 3.0   | <ul><li>'NEW 폴레드 픽셀 UVC LED 자연건조 유아소독기 픽셀+맘마존트레이_오프화이트 바디_더스티핑크  패널 출산/육아 > 소독/살균용품 > 젖병소독기'</li><li>'해님 UV LED 젖병소독기 4세대 플러스 스마트 플렉스_화이트 출산/육아 > 소독/살균용품 > 젖병소독기'</li><li>'NEW 폴레드 픽셀 UVC LED 자연건조 유아소독기 픽셀+맘마존트레이_다크그레이 바디_오프화이트  패널 출산/육아 > 소독/살균용품 > 젖병소독기'</li></ul> |
| 5.0   | <ul><li>'브러쉬 세트 스폰지 알프스  출산/육아 > 소독/살균용품 > 젖병솔'</li><li>'마더케이 리필형 롱핸들 스펀지 실리콘 젖병솔 젖꼭지솔 세트 7.리필형 스펀지 젖병솔+젖꼭지솔 풀세트_인디핑크 출산/육아 > 소독/살균용품 > 젖병솔'</li><li>'스펙트라 롱타입 젖꼭지솔 2개입세트 젖병 물병 텀블러 세척가능 1.스펙트라 롱타입 젖꼭지솔 2개입 [핑크] 출산/육아 > 소독/살균용품 > 젖병솔'</li></ul>                        |
| 0.0   | <ul><li>'자외선 장난감 살균기SW-400열풍건조 소독기  출산/육아 > 소독/살균용품 > 장난감소독기'</li><li>'대신 DS-930 앞치마 살균 소독건조기  출산/육아 > 소독/살균용품 > 장난감소독기'</li><li>'소독 캐비닛 양문형 교재 살균기 자외선 UV 컵 소독기 700-GX1 이중문 및 이중서랍(나노대리석) 출산/육아 > 소독/살균용품 > 장난감소독기'</li></ul>                                            |
| 1.0   | <ul><li>'젖병건조대 수납박스 물컵 젖병선반 보관함 브라운/2318 출산/육아 > 소독/살균용품 > 젖병건조대'</li><li>'뉴코코맘 프리미엄 젖병건조대 2단 뚜껑 이유식기건조대 1. 뉴코코맘 젖병건조대 프리미엄_1단 그레이 출산/육아 > 소독/살균용품 > 젖병건조대'</li><li>'마더케이 리필형 롱핸들 실리콘솔 세척용품세트(건조대+리필세척솔+집게) 네이비_젖병솔&젖꼭지솔_인디핑크 출산/육아 > 소독/살균용품 > 젖병건조대'</li></ul>          |
| 6.0   | <ul><li>'실리콘 젖병소독집게 분유 열탕 가위 건조기 살균 수저받침_올리브그레이 출산/육아 > 소독/살균용품 > 젖병집게'</li><li>'더굿즈 다용도 젖병 소독 집게 핑크 핑크 출산/육아 > 소독/살균용품 > 젖병집게'</li><li>'젖병집게 젖병소독집게 아기 유아 다용도 페블식탁매트(조약돌)_골드크림 출산/육아 > 소독/살균용품 > 젖병집게'</li></ul>                                                         |
| 4.0   | <ul><li>'오스람 실바니아 살균램프 4W 젖병소독기 자외선램프 UV램프  출산/육아 > 소독/살균용품 > 젖병소독기용품'</li><li>'스펙트라 젖병소독기호환 UV 램프 1개입 4W / 실바니아  출산/육아 > 소독/살균용품 > 젖병소독기용품'</li><li>'효성 자외선살균램프 20W UVC 살균등 젖병소독기 컵소독기 식기소독기 1개 G20T8  출산/육아 > 소독/살균용품 > 젖병소독기용품'</li></ul>                              |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9978   |

## 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_bc7")
# Run inference
preds = model("비앤비 유아 아기 젖병세정제 거품형 용기  출산/육아 > 소독/살균용품 > 젖병세정제")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 7   | 13.7494 | 25  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 70                    |
| 1.0   | 70                    |
| 2.0   | 70                    |
| 3.0   | 70                    |
| 4.0   | 19                    |
| 5.0   | 70                    |
| 6.0   | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0116  | 1    | 0.4771        | -               |
| 0.5814  | 50   | 0.4546        | -               |
| 1.1628  | 100  | 0.1706        | -               |
| 1.7442  | 150  | 0.0404        | -               |
| 2.3256  | 200  | 0.0101        | -               |
| 2.9070  | 250  | 0.0093        | -               |
| 3.4884  | 300  | 0.0082        | -               |
| 4.0698  | 350  | 0.0062        | -               |
| 4.6512  | 400  | 0.0001        | -               |
| 5.2326  | 450  | 0.0           | -               |
| 5.8140  | 500  | 0.0           | -               |
| 6.3953  | 550  | 0.0           | -               |
| 6.9767  | 600  | 0.0           | -               |
| 7.5581  | 650  | 0.0           | -               |
| 8.1395  | 700  | 0.0           | -               |
| 8.7209  | 750  | 0.0           | -               |
| 9.3023  | 800  | 0.0           | -               |
| 9.8837  | 850  | 0.0           | -               |
| 10.4651 | 900  | 0.0           | -               |
| 11.0465 | 950  | 0.0           | -               |
| 11.6279 | 1000 | 0.0           | -               |
| 12.2093 | 1050 | 0.0           | -               |
| 12.7907 | 1100 | 0.0           | -               |
| 13.3721 | 1150 | 0.0           | -               |
| 13.9535 | 1200 | 0.0           | -               |
| 14.5349 | 1250 | 0.0           | -               |
| 15.1163 | 1300 | 0.0           | -               |
| 15.6977 | 1350 | 0.0           | -               |
| 16.2791 | 1400 | 0.0           | -               |
| 16.8605 | 1450 | 0.0           | -               |
| 17.4419 | 1500 | 0.0           | -               |
| 18.0233 | 1550 | 0.0           | -               |
| 18.6047 | 1600 | 0.0           | -               |
| 19.1860 | 1650 | 0.0           | -               |
| 19.7674 | 1700 | 0.0           | -               |
| 20.3488 | 1750 | 0.0           | -               |
| 20.9302 | 1800 | 0.0           | -               |
| 21.5116 | 1850 | 0.0           | -               |
| 22.0930 | 1900 | 0.0           | -               |
| 22.6744 | 1950 | 0.0           | -               |
| 23.2558 | 2000 | 0.0           | -               |
| 23.8372 | 2050 | 0.0           | -               |
| 24.4186 | 2100 | 0.0           | -               |
| 25.0    | 2150 | 0.0           | -               |
| 25.5814 | 2200 | 0.0           | -               |
| 26.1628 | 2250 | 0.0           | -               |
| 26.7442 | 2300 | 0.0           | -               |
| 27.3256 | 2350 | 0.0           | -               |
| 27.9070 | 2400 | 0.0           | -               |
| 28.4884 | 2450 | 0.0           | -               |
| 29.0698 | 2500 | 0.0           | -               |
| 29.6512 | 2550 | 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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