setfit safetensors roberta sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:mini1013/master_domain base_model:finetune:mini1013/master_domain model-index region:us
---
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: 루핀 젤크리너 1000ml 젤리무버 아세톤 젤클리너 루핀젤리무버1000ml 건강드림
- text: 요거트젤 버니츄 s63 베리츄 봄컬러 파스텔시럽젤 S56 핑크츄 더메이트
- text: 코스노리 컬러테라피 네일세럼 4ml 01 시트러스 (주)그레이스클럽
- text: 더젤 젤리무버 더젤 젤리무버 + 오팔스톤2알 주식회사 이룸
- text: 리본머리핀 태닝키티네일파츠(1개입)1-핑크리본머리핀 레드 리본머리핀(1개입) 올리비아수(oliviasoo)
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: 0.6072186836518046
      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                                                                                                                                                                                                            |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 6.0   | <ul><li>'요거트네일 젤네일 화양연화 9종세트 글리터컬러 시럽컬러 옵션없음 주식회사 코즈랩'</li><li>'프롬더네일 로코 핑크 자석젤 자석네일 단품 진주 2알 FG130+진주 2알 백억언니'</li><li>'루벤스 바르면 펴지는 딱 올려젤 10ml 3개입 내성발톱 문제성발톱 옵션없음 제네시스오브네일'</li></ul>                             |
| 5.0   | <ul><li>'[1+1] 데싱디바 글레이즈 여름 최신상 젤네일&페디 DASHING DIVA'</li><li>'잇템샵 네일팁 인조손톱 패디팁 붙이는네일아트 페디큐어 브라이트핑크 내가원하는잇템샵'</li><li>'크레아 네일 디자인팁 수제팁 택1 DMC 네일아트재료'</li></ul>                                                      |
| 1.0   | <ul><li>'모양89 스톤와이어 리본 네일스티커 블루믹스 (AF-01) 단지네 네일가게'</li><li>'태닝키티파츠 TKT-02-08 썬탠키티 5개입 탄 갸루 하와이 비키니 태닝키티파츠 TKT-02-01 5개입 임프주식회사'</li><li>'네일아트 리필팁 네일팁 숏오발 A타입클리어1호-50개입 풀팁_1.클리어_8호(8.2X21mm) 단지네 네일가게'</li></ul>  |
| 0.0   | <ul><li>'블루크로스 큐티클리무버 32oz+뾰족캡 공병 32oz （＋뾰족캡 공병 증정♥） 주식회사 시그니처바스켓(SIGNATURE BASKET)'</li><li>'루핀 젤클리너 젤리무버 500ml 아세톤 젤클렌져 루핀젤리무버500ml 신나라닷컴'</li><li>'블루크로스 큐티클 리무버 6oz 리무버 오일펜 공병 6oz＋오일펜1개＋공병1개 2N(투엔)'</li></ul> |
| 3.0   | <ul><li>'손톱깎이 클리퍼 세트 가정용 관리 기기 Green 4-piece set 영무몰'</li><li>'Coms LED 손톱깎이돋보기CW-816 조명 KW6E00D3 옵션없음 하니스토어13'</li><li>'메이보릿 메보카세 브러쉬 셋트 , 실버글로시 옵션없음 마법사네일'</li></ul>                                             |
| 4.0   | <ul><li>'[위드샨] 맞춤 케어 2종 세트 (3타입 중 택1) 잘 부러지고 약한 손톱(스트랭쓰너+쉴드탑) 주식회사손과발'</li><li>'셀프 젤네일 세트 홈 키트 로나네일'</li><li>'루카너스 프리미엄구성 여자친구선물 셀프네일세트 큐티클제거 손톱관리 네일세트 9종 1박스 루카너스'</li></ul>                                      |
| 2.0   | <ul><li>'퍼펙토 발톱연화제 나겔바이셔 20ml 발톱연화제 1개+2in1 큐렛&샤퍼 1개 주식회사 킹케어(KINGCAIR Co., Ltd.)'</li><li>'뉴 요피클리어 13ml 핑거스 문제성 손발톱관리 리뉴얼 세럼 옵션없음 제이비컴퍼니'</li><li>'케라셀 패치 14매 나이트타임 손발톱영양제 손발톱 강화제 옵션없음 행운'</li></ul>              |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.6072   |

## 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_bt1_test")
# Run inference
preds = model("더젤 젤리무버 더젤 젤리무버 + 오팔스톤2알 주식회사 이룸")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 4   | 9.3955 | 18  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 16                    |
| 1.0   | 19                    |
| 2.0   | 21                    |
| 3.0   | 32                    |
| 4.0   | 10                    |
| 5.0   | 16                    |
| 6.0   | 20                    |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0625 | 1    | 0.4888        | -               |
| 3.125  | 50   | 0.3006        | -               |
| 6.25   | 100  | 0.0746        | -               |
| 9.375  | 150  | 0.0192        | -               |
| 12.5   | 200  | 0.0002        | -               |
| 15.625 | 250  | 0.0001        | -               |
| 18.75  | 300  | 0.0001        | -               |
| 21.875 | 350  | 0.0001        | -               |
| 25.0   | 400  | 0.0001        | -               |
| 28.125 | 450  | 0.0           | -               |
| 31.25  | 500  | 0.0           | -               |
| 34.375 | 550  | 0.0           | -               |
| 37.5   | 600  | 0.0           | -               |
| 40.625 | 650  | 0.0           | -               |
| 43.75  | 700  | 0.0           | -               |
| 46.875 | 750  | 0.0           | -               |
| 50.0   | 800  | 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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