---
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: COLOR WOW Xtra 대형 봄쉘 볼류마이저 6.5 Ounce 6.5 Ounce 모모나미
- text: 헤어젤슈퍼하드400ml 과일나라 컨퓸 MWB794D8 옵션없음 하니스토어04
- text: 메온셀 GRAFEN 다운펌약 남자다운펌 옆머리누르기 셀프매직약 A 세일몬스터
- text: '[6월7일 이후 배송] 브리티시엠 어반 매트 클레이 100g / URBAN MATTE CLAY 헤어 왁스 미용실 강력 짧은머리 고정
    남자머리 셋팅 선택X (파우치 필요없어요) (주)컨템포'
- text: Aveda Phomollient Styling Foam 6.7 oz (관부가세포함) 옵션없음 제이글로벌컴퍼니
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: 0.7192224622030238
      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:** 6 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                                                                                                                                                                                                        |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0.0   | <ul><li>'MANIC PANIC 매닉 패닉 Bad Boy Blue 배드 보이 블루 옵션없음 제이(J) 커머스'</li><li>'미쟝센 올뉴 쉽고빠른 거품 염색약 5N 갈색 1개 옵션없음 트레이딩제이'</li><li>'376252 씨드비 물염색 시즌2 씨비드 4회분 미디엄브라운 NEW 비건 미디엄 브라운 1박스_◈232431989◈ 제이제이홀딩스'</li></ul> |
| 3.0   | <ul><li>'로레알 테크니아트 픽스 디자인 스프레이 200ml 옵션없음 파스텔뷰티'</li><li>'과일나라 컨퓸 슈퍼하드 워터스프레이 252ml 옵션없음 다인유통'</li><li>'폴미첼 프리즈 앤 슈퍼 샤인 스프레이 250ml 옵션없음 다사다 유한책임회사'</li></ul>                                                   |
| 4.0   | <ul><li>'미쟝센 파워스윙 슈퍼하드 크림 왁스 9 미디움 리젠트업 80g 옵션없음 와라즈'</li><li>'Loma Hair Care 3525927124 LOMA 포밍 페이스트 85g(3온스) 옵션없음 넥스유로(NEXEURO)'</li><li>'차홍 왁스 쉬폰 소프트 80ml 부드러운 크림제형 옵션없음 박예찬'</li></ul>                     |
| 1.0   | <ul><li>'모레모 케라틴 셀프 다운 펌 6개 100g 옵션없음 건강드림'</li><li>'다주자 울트라 다운펌150ml 남자다운펌 여성매직펌 잔머리펌 다운펌set 옵션없음 포비티엘'</li><li>'미용실 다운펌약 집에서 옆머리 누르기 올리브영 악성곱슬 남자 셀프 다운펌 옵션없음 새벽 마트'</li></ul>                                |
| 5.0   | <ul><li>'꽃을든남자 초강력헤어젤 500ml 옵션없음 태은코리아'</li><li>'lg생활건강 아르드포 헤어젤 펌프형 300ml 옵션없음 맥센 트레이드'</li><li>'Ecoco 에코 스타일러 크리스탈 스타일링 젤 453g (3팩) 옵션없음 세렌몰1'</li></ul>                                                      |
| 2.0   | <ul><li>'밀본 니제르 클러치피즈 하이 클러치피즈 200g 헤어무스 헤어팟'</li><li>'갸스비 수퍼하드 스타일링폼 무스 185ml 홈쇼핑 동일상품 수퍼하드 스타일링폼 무스 185ml 제이에스유통'</li><li>'꽃을든남자 스타일링 헤어 무스 300ml 퀸뷰티'</li></ul>                                              |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.7192   |

## 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_bt11_test")
# Run inference
preds = model("헤어젤슈퍼하드400ml 과일나라 컨퓸 MWB794D8 옵션없음 하니스토어04")
```

<!--
### Downstream Use

*List how someone could finetune this model on their own dataset.*
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 5   | 9.4957 | 26  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 25                    |
| 1.0   | 19                    |
| 2.0   | 15                    |
| 3.0   | 25                    |
| 4.0   | 19                    |
| 5.0   | 14                    |

### Training Hyperparameters
- batch_size: (512, 512)
- num_epochs: (50, 50)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 60
- 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0714  | 1    | 0.4886        | -               |
| 3.5714  | 50   | 0.3088        | -               |
| 7.1429  | 100  | 0.049         | -               |
| 10.7143 | 150  | 0.0043        | -               |
| 14.2857 | 200  | 0.0001        | -               |
| 17.8571 | 250  | 0.0001        | -               |
| 21.4286 | 300  | 0.0001        | -               |
| 25.0    | 350  | 0.0001        | -               |
| 28.5714 | 400  | 0.0001        | -               |
| 32.1429 | 450  | 0.0001        | -               |
| 35.7143 | 500  | 0.0001        | -               |
| 39.2857 | 550  | 0.0001        | -               |
| 42.8571 | 600  | 0.0001        | -               |
| 46.4286 | 650  | 0.0001        | -               |
| 50.0    | 700  | 0.0001        | -               |

### 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->