README.md

---
base_model: mini1013/master_domain
library_name: setfit
metrics:
- metric
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 길이조절 안경고정 밴드 코받침 패드 운동 캠핑 등산 진브라운 알리몽드
- text: 레이밴 안경테 RB3691VF 2509 남자 여자 동그란안경 아시안핏  시온아이엔티
- text: 밀착 스포츠안경줄 흔들림방지 안경스트랩  비앤비
- text: '[텐바이텐]바체타팩토리 가죽 안경 케이스 08 오렌지_추가 안 함_추가 안 함 신세계몰'
- text: TUMI 투미 카본 티타늄 명품 안경테 메탈 스퀘어 남자 여자 공용 안경 04.TU10-0003-01 LFmall02
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: metric
      value: 0.9104360692836626
      name: Metric
---

# 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                                                                                                                                                                                                                         |
|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 5.0   | <ul><li>'초경량 국산 안경테 베타 울템 카본 티타늄 뿔테안경 551-599_S571-2 브라운투톤 ENA아이웨어'</li><li>'B019 ORIGINAL GLASS CRYSTAL GREEN  '</li><li>'니시데카즈오 BROWLINE2 하금테 근적외선 차단렌즈 아이라이크(EYE LIKE)'</li></ul>                                             |
| 1.0   | <ul><li>'레더렛소가죽선글라스파우치휴대용안경케이스  이정민'</li><li>'위에 안경 쓰는 파우치 편광 끼우는 선글라스 3종 세트 선그라스 클립 에끼우는 플립 온 클립선글라스3종세트_일반블랙 홉포엘'</li><li>'휴대용 가죽 선글라스 안경 파우치 케이스 보관함 안 PU안경케이스_그레이 라이프패션'</li></ul>                                           |
| 3.0   | <ul><li>'아이업꽈배기인조가죽안경줄10p세트선글라스줄  유어드림커머스'</li><li>'스트랩 캐주얼디자인줄 스토퍼줄 안경걸이 끈 B 더펭귄샵'</li><li>'천연 크리스탈 안경 선글라스 걸이 줄 원석 비즈 빈티지 에스닉 마스크 스트랩 겸용 블루  3mm  70-75CM nouville'</li></ul>                                                  |
| 0.0   | <ul><li>'갤러리아 NIRNIR SUNGLASS 5 COLOR GREEN 갤러리아몰'</li><li>'여자 켓아이 뿔테 선그라스 썬그라스 남자 RORGGE 2111 상품선택_2유광블랙 온달이'</li><li>'뮤즈 서클 뿔테선글라스 코코아 푸치백'</li></ul>                                                                          |
| 2.0   | <ul><li>'로에드 안경 자국 코패드 코받침 눌림 선글라스 코 통증 방지 패드 교체 스티커 안경코패드 1.8mm（화이트）_2.8mm(화이트) 로에드'</li><li>'[힐포]국산 고급 초극세사 렌즈 안경닦이 김서림방지 클리너 크리너 악기수건 안경천 융s 05. knit 안경닦이30매 15x18cm_블루 모아텍스'</li><li>'자우버 렌즈 케어 클리닝 티슈 200매  메디위'</li></ul> |
| 4.0   | <ul><li>'산리오 안경정리함 안경케이스 세트 6종 안경케이스시나모롤 지에이치글로벌'</li><li>'(이거찜) 프리미엄 가죽 안경집 안경케이스 가죽안경집 스카이 제이케이'</li><li>'스트랩 안경케이스 휴대용 안경파우치 가죽안경보관집 선글라스보관케이스 No.01 스트랩 안경케이스 블랙 여선영'</li></ul>                                              |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.9104 |

## 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_ac4")
# Run inference
preds = model("밀착 스포츠안경줄 흔들림방지 안경스트랩  비앤비")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 9.53   | 20  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 50                    |
| 1.0   | 50                    |
| 2.0   | 50                    |
| 3.0   | 50                    |
| 4.0   | 50                    |
| 5.0   | 50                    |

### Training Hyperparameters
- batch_size: (512, 512)
- num_epochs: (20, 20)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 40
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch   | Step | Training Loss | Validation Loss |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0213  | 1    | 0.4524        | -               |
| 1.0638  | 50   | 0.2583        | -               |
| 2.1277  | 100  | 0.0642        | -               |
| 3.1915  | 150  | 0.0781        | -               |
| 4.2553  | 200  | 0.0806        | -               |
| 5.3191  | 250  | 0.0391        | -               |
| 6.3830  | 300  | 0.0011        | -               |
| 7.4468  | 350  | 0.0003        | -               |
| 8.5106  | 400  | 0.0001        | -               |
| 9.5745  | 450  | 0.0001        | -               |
| 10.6383 | 500  | 0.0           | -               |
| 11.7021 | 550  | 0.0           | -               |
| 12.7660 | 600  | 0.0           | -               |
| 13.8298 | 650  | 0.0           | -               |
| 14.8936 | 700  | 0.0           | -               |
| 15.9574 | 750  | 0.0           | -               |
| 17.0213 | 800  | 0.0           | -               |
| 18.0851 | 850  | 0.0           | -               |
| 19.1489 | 900  | 0.0           | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0.dev0
- Sentence Transformers: 3.1.1
- Transformers: 4.46.1
- PyTorch: 2.4.0+cu121
- Datasets: 2.20.0
- Tokenizers: 0.20.0

## 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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