---
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: 헤어샵 전용 바이오메드 엘피피 트리트먼트 LPP 실크 트리트먼트1000ml 사은품 증정  (#M)쿠팡 홈>뷰티>헤어>트리트먼트/팩>일반
    트리트먼트 Coupang > 뷰티 > 헤어 > 트리트먼트/팩 > 일반 트리트먼트
- text: 미쟝센 퍼펙트 세럼 트리트먼트 330ml × 1개 (#M)쿠팡 홈>뷰티>헤어>트리트먼트/팩/앰플>일반 트리트먼트 Coupang > 뷰티
    > 헤어 > 트리트먼트/팩/앰플 > 일반 트리트먼트
- text: 한소희Pick 로레알파리 토탈리페어5 트리트먼트 헤어팩 400ml 50ml 헤어팩280ml LotteOn > 뷰티 > 헤어/바디 >
    헤어케어 > 트리트먼트/헤어팩 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 트리트먼트/헤어팩
- text: 밀크바오밥 오리지널 샴푸 화이트솝 1L(옵션선택1) 11 트리트먼트 화이트솝 1000ml (#M)헤어케어>샴푸>샴푸바 AD > traverse
    > 11st > 뷰티 > 헤어케어 > 샴푸 > 샴푸바
- text: 로레알 토탈리페어5 헤어팩 280ml + 170ml  (#M)쿠팡 홈>생활용품>헤어/바디/세안>트리트먼트/팩/앰플>헤어팩/헤어마스크
    Coupang > 뷰티 > 헤어 > 트리트먼트/팩/앰플 > 헤어팩/헤어마스크
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: 0.8786919831223629
      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:** 2 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     | <ul><li>'[웰라] 염색모전용 SP 컬러 세이브 마스크 400ml  (#M)화장품/미용>헤어케어>헤어팩 LO > window_fashion_town > Naverstore > FashionTown > 뷰티 > CATEGORY > 헤어케어 > 트리트먼트/팩 > 헤어팩'</li><li>'아모스 01 퓨어스마트 샴푸 팩 비듬케어 사춘기샴푸 퓨어 스마트 팩 300ml-비듬두피팩 (#M)홈>화장품/미용>헤어케어>샴푸 Naverstore > 화장품/미용 > 헤어케어 > 샴푸'</li><li>'미쟝센 데미지 케어 로즈프로틴 헤어팩 150ml × 1개 (#M)쿠팡 홈>생활용품>헤어/바디/세안>트리트먼트/팩/앰플>헤어팩/헤어마스크 Coupang > 뷰티 > 헤어 > 트리트먼트/팩/앰플 > 헤어팩/헤어마스크'</li></ul> |
| 0     | <ul><li>'스무드 인퓨전 너리싱 스타일링 크림 250ml  LotteOn > 뷰티 > 명품화장품 > 헤어케어 LotteOn > 뷰티 > 헤어케어 > 헤어에센스'</li><li>'체리블라썸/아르간오일 트리트먼트 280ml x2개 02)모로코아르간 트리트먼트 2개 LotteOn > 뷰티 > 헤어케어 > 트리트먼트 LotteOn > 뷰티 > 헤어케어 > 트리트먼트'</li><li>'[LG생활건강] 비욘드 프로페셔널 디펜스 트리트먼트 500ml  LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 린스 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 린스'</li></ul>                                                                              |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.8787   |

## 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_top_bt13_9_test_flat")
# Run inference
preds = model("미쟝센 퍼펙트 세럼 트리트먼트 330ml × 1개 (#M)쿠팡 홈>뷰티>헤어>트리트먼트/팩/앰플>일반 트리트먼트 Coupang > 뷰티 > 헤어 > 트리트먼트/팩/앰플 > 일반 트리트먼트")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 11  | 21.07  | 49  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 50                    |
| 1     | 50                    |

### Training Hyperparameters
- batch_size: (64, 64)
- num_epochs: (30, 30)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 100
- 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0064  | 1    | 0.4262        | -               |
| 0.3185  | 50   | 0.4176        | -               |
| 0.6369  | 100  | 0.314         | -               |
| 0.9554  | 150  | 0.0953        | -               |
| 1.2739  | 200  | 0.0302        | -               |
| 1.5924  | 250  | 0.0123        | -               |
| 1.9108  | 300  | 0.0005        | -               |
| 2.2293  | 350  | 0.0002        | -               |
| 2.5478  | 400  | 0.0001        | -               |
| 2.8662  | 450  | 0.0001        | -               |
| 3.1847  | 500  | 0.0001        | -               |
| 3.5032  | 550  | 0.0           | -               |
| 3.8217  | 600  | 0.0001        | -               |
| 4.1401  | 650  | 0.0           | -               |
| 4.4586  | 700  | 0.0           | -               |
| 4.7771  | 750  | 0.0           | -               |
| 5.0955  | 800  | 0.0001        | -               |
| 5.4140  | 850  | 0.0001        | -               |
| 5.7325  | 900  | 0.0           | -               |
| 6.0510  | 950  | 0.0           | -               |
| 6.3694  | 1000 | 0.0           | -               |
| 6.6879  | 1050 | 0.0           | -               |
| 7.0064  | 1100 | 0.0           | -               |
| 7.3248  | 1150 | 0.0           | -               |
| 7.6433  | 1200 | 0.0           | -               |
| 7.9618  | 1250 | 0.0           | -               |
| 8.2803  | 1300 | 0.0           | -               |
| 8.5987  | 1350 | 0.0           | -               |
| 8.9172  | 1400 | 0.0           | -               |
| 9.2357  | 1450 | 0.0           | -               |
| 9.5541  | 1500 | 0.0           | -               |
| 9.8726  | 1550 | 0.0           | -               |
| 10.1911 | 1600 | 0.0           | -               |
| 10.5096 | 1650 | 0.0           | -               |
| 10.8280 | 1700 | 0.0           | -               |
| 11.1465 | 1750 | 0.0           | -               |
| 11.4650 | 1800 | 0.0           | -               |
| 11.7834 | 1850 | 0.0           | -               |
| 12.1019 | 1900 | 0.0           | -               |
| 12.4204 | 1950 | 0.0           | -               |
| 12.7389 | 2000 | 0.0           | -               |
| 13.0573 | 2050 | 0.0           | -               |
| 13.3758 | 2100 | 0.0           | -               |
| 13.6943 | 2150 | 0.0           | -               |
| 14.0127 | 2200 | 0.0           | -               |
| 14.3312 | 2250 | 0.0           | -               |
| 14.6497 | 2300 | 0.0           | -               |
| 14.9682 | 2350 | 0.0           | -               |
| 15.2866 | 2400 | 0.0           | -               |
| 15.6051 | 2450 | 0.0           | -               |
| 15.9236 | 2500 | 0.0           | -               |
| 16.2420 | 2550 | 0.0           | -               |
| 16.5605 | 2600 | 0.0           | -               |
| 16.8790 | 2650 | 0.0           | -               |
| 17.1975 | 2700 | 0.0001        | -               |
| 17.5159 | 2750 | 0.0001        | -               |
| 17.8344 | 2800 | 0.0003        | -               |
| 18.1529 | 2850 | 0.0           | -               |
| 18.4713 | 2900 | 0.0           | -               |
| 18.7898 | 2950 | 0.0           | -               |
| 19.1083 | 3000 | 0.0           | -               |
| 19.4268 | 3050 | 0.0           | -               |
| 19.7452 | 3100 | 0.0001        | -               |
| 20.0637 | 3150 | 0.0002        | -               |
| 20.3822 | 3200 | 0.0           | -               |
| 20.7006 | 3250 | 0.0           | -               |
| 21.0191 | 3300 | 0.0           | -               |
| 21.3376 | 3350 | 0.0           | -               |
| 21.6561 | 3400 | 0.0           | -               |
| 21.9745 | 3450 | 0.0           | -               |
| 22.2930 | 3500 | 0.0           | -               |
| 22.6115 | 3550 | 0.0           | -               |
| 22.9299 | 3600 | 0.0           | -               |
| 23.2484 | 3650 | 0.0           | -               |
| 23.5669 | 3700 | 0.0           | -               |
| 23.8854 | 3750 | 0.0           | -               |
| 24.2038 | 3800 | 0.0           | -               |
| 24.5223 | 3850 | 0.0           | -               |
| 24.8408 | 3900 | 0.0           | -               |
| 25.1592 | 3950 | 0.0           | -               |
| 25.4777 | 4000 | 0.0           | -               |
| 25.7962 | 4050 | 0.0           | -               |
| 26.1146 | 4100 | 0.0           | -               |
| 26.4331 | 4150 | 0.0           | -               |
| 26.7516 | 4200 | 0.0           | -               |
| 27.0701 | 4250 | 0.0           | -               |
| 27.3885 | 4300 | 0.0           | -               |
| 27.7070 | 4350 | 0.0           | -               |
| 28.0255 | 4400 | 0.0           | -               |
| 28.3439 | 4450 | 0.0           | -               |
| 28.6624 | 4500 | 0.0           | -               |
| 28.9809 | 4550 | 0.0           | -               |
| 29.2994 | 4600 | 0.0           | -               |
| 29.6178 | 4650 | 0.0           | -               |
| 29.9363 | 4700 | 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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