File size: 8,985 Bytes

33b868c

---
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: '[PS5] 딥 어스 디스크에디션 콘솔 커버 코발트 블루  오진상사(주)'
- text: '[PS5] 플레이스테이션5 디스크 에디션  오진상사(주)'
- text: PS4 그란투리스모 스포트 한글판 PlaystationHits  조이게임
- text: PS4 아이돌마스터 스탈릿 시즌 일반판 새제품 한글판  제이와이게임타운
- text: '[PS4] 색보이 빅 어드벤처  에이티게임(주)'
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: 0.7771822358346095
      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:** 5 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                                                                                                                                                                                                              |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 3     | <ul><li>'[PS4] NBA 2K24 코비 브라이언트 에디션 특전 바우처 有  오진상사(주)'</li><li>'닌텐도 스위치 둘이서 냥코 대전쟁 한글판  게임매니아'</li><li>'닌텐도 마리오 카트 8 디럭스 + 조이콘 휠 패키지 SWITCH 한글판 마리오카트8 디럭스 (+조이콘핸들 세트)_마리오카트8 (+핸들 2개 원형 네온) 주식회사 쇼핑랩스'</li></ul>    |
| 2     | <ul><li>'[트러스트마스터] T80 Ferrari 488 GTB 에디션  주식회사 투비네트웍스글로벌'</li><li>'트러스트마스터 T300 페라리 Integral 레이싱휠 [PS5, PS4, PC지원]  주식회사 디에스샵(DS SHOP)'</li><li>'레이저코리아 울버린 V2 크로마 Wolverine V2 Chroma 게임 컨트롤러  (주)하이케이넷'</li></ul> |
| 1     | <ul><li>'[노리박스] 오락실 게임기 분리기통(고급DX팩)  (주)에스와이에스리테일'</li><li>'[XBOX]마이크로 소프트 정식발매 X-BOX series X 1TB 새제품  다음텔레콤'</li><li>'노리박스 32인치 스탠드형 강화유리 오락실게임기 오락기 DX팩(3000게임/720P/3~4인지원) (주)노리박스게임연구소'</li></ul>                |
| 0     | <ul><li>'PC 삼국지 14 한글판 (스팀코드발송)  (주) 디지털터치'</li><li>'Wizard with a Gun 스팀 PC 뉴 어카운트 (정지X) / 기존계정 가능 기존 계정 스팀 유통할인'</li><li>'철권7 tekken7 PC/스팀 철권7 (코드48시이내발송)  전한수'</li></ul>                                         |
| 4     | <ul><li>'한국 닌텐도 정품 게임기 스위치 신형 OLED+콘트라 로그콥스+액정강화유리세트 OLED 네온레드블루 색상_OLED본체+뉴슈퍼마리오U디럭스+강화유리 에이지씨'</li><li>'게임&워치 젤다의 전설  주식회사 손오공'</li><li>'닌텐도 스위치 라이트 옐로 동물의 숲 케이스  주식회사 손오공'</li></ul>                              |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.7772 |

## 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_el3")
# Run inference
preds = model("[PS4] 색보이 빅 어드벤처  에이티게임(주)")
```

<!--
### 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   | 10.7325 | 23  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 43                    |
| 1     | 50                    |
| 2     | 50                    |
| 3     | 50                    |
| 4     | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0263  | 1    | 0.496         | -               |
| 1.3158  | 50   | 0.1186        | -               |
| 2.6316  | 100  | 0.0532        | -               |
| 3.9474  | 150  | 0.0398        | -               |
| 5.2632  | 200  | 0.0002        | -               |
| 6.5789  | 250  | 0.0001        | -               |
| 7.8947  | 300  | 0.0001        | -               |
| 9.2105  | 350  | 0.0001        | -               |
| 10.5263 | 400  | 0.0001        | -               |
| 11.8421 | 450  | 0.0001        | -               |
| 13.1579 | 500  | 0.0001        | -               |
| 14.4737 | 550  | 0.0001        | -               |
| 15.7895 | 600  | 0.0           | -               |
| 17.1053 | 650  | 0.0001        | -               |
| 18.4211 | 700  | 0.0001        | -               |
| 19.7368 | 750  | 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}
}
```

<!--
## 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.*
-->