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: 원목 듀얼 모니터받침대 미송 B타입 M  주식회사 제이테크(J-TECH)
- text: 대형 게이밍모니터거치대 카멜마운트 PMA-2U USB지원 32인치 거치가능 모니터암 블랙 (주)순천물류
- text: 카멜마운트 CMA2V 듀얼 벽면 밀착형 상하 거치대 모니터암 블랙 주식회사 카멜인터내셔널
- text: 알파스캔 AOC AM400 시에라 블루 싱글 모니터암 컴퓨터 27인치 32인치 브라켓 AM400 로즈쿼츠 주식회사 멀티스캔텍
- text: 카멜인터내셔널 카멜마운트 고든 DMA-DSS 벽면 밀착형 듀얼 모니터암  (주)아이티엔조이
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: 0.8586497890295358
      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     | <ul><li>'(주)근호컴 [리버네트워크]USB 2.0 리피터 전용 전원 어댑터 (NX-USBEXPW)  (주)근호컴'</li><li>'NEXI 넥시 정품 NX-USBEXPW아답터 (NX0284)  (주)유니정보통신'</li><li>'국산 12V 5A 모니터 아답터 ML-125A  헤라유통'</li></ul>                                               |
| 3     | <ul><li>'카멜마운트 GDA3 고든 디자인 모니터 거치대 모니터암 듀얼 블랙 주식회사 카멜인터내셔널'</li><li>'카멜 CA2 화이트 나뭉'</li><li>'마루느루 마운트뷰 MV-G1A  셜크'</li></ul>                                                                                                 |
| 0     | <ul><li>'셋탑 박스 게임기 리모컨 수납 TV 모니터 TOP 공간 선반 공유기 거치대  아이디어윙'</li><li>'리모컨수납 TV 모니터 TOP 공간선반 Black 연상연하'</li><li>'애니포트 TV거치대 엘마운트 다용도 멀티 선반 S900  이스토어'</li></ul>                                                               |
| 1     | <ul><li>'ELLOVEN 엘로벤 모니터스탠드+서랍 엘로벤 스탠드 앤트러 (804.851.02) 랩앤툴스'</li><li>'썬엔원 유보드 모니터받침대 U-BOARD Basic [화이트] 강화유리 / 유리색상: 투명 블랙 (주)세븐앤씨'</li><li>'앱코 MES100 사이드 폴딩 모니터 받침대 선반 받침 서랍 데스크 정리 블랙 앱코 MES100 블랙 (주)드림팩토리샵'</li></ul> |
| 2     | <ul><li>'아이존아이앤디 EZ MSM-10  아이러브드라이브(I Love Drive)'</li><li>'아이존아이앤디 EZ MSM-10/EZ MSM-10/조절브라켓/모니터스탠드/높낮이조절/조절스탠드/모니터홀타입/홀타입스탠드 EZ MSM-10 기쁘다희샵'</li><li>'루나랩 베사확장브라켓 200x100 200x200  주식회사 루나'</li></ul>                    |
| 4     | <ul><li>'지클릭커 휴 쉴드 PET 부착식 정보보호 모니터 보안필름 22인치  가이드컴퓨터'</li><li>'힐링쉴드 11890340 22인치 모니터 블루라이트차단 보호필름 거치식 조립형 양면필터  온라인정품인증점'</li><li>'지클릭커 휴 쉴드 PET 부착식 정보보호 모니터 보안필름 22인치  주식회사 리더샵'</li></ul>                               |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.8586 |

## 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_el10")
# Run inference
preds = model("원목 듀얼 모니터받침대 미송 B타입 M  주식회사 제이테크(J-TECH)")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 4   | 9.9725 | 24  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 50                    |
| 1     | 50                    |
| 2     | 13                    |
| 3     | 50                    |
| 4     | 5                     |
| 5     | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0286  | 1    | 0.4958        | -               |
| 1.4286  | 50   | 0.0386        | -               |
| 2.8571  | 100  | 0.0016        | -               |
| 4.2857  | 150  | 0.0001        | -               |
| 5.7143  | 200  | 0.0           | -               |
| 7.1429  | 250  | 0.0           | -               |
| 8.5714  | 300  | 0.0           | -               |
| 10.0    | 350  | 0.0           | -               |
| 11.4286 | 400  | 0.0001        | -               |
| 12.8571 | 450  | 0.0           | -               |
| 14.2857 | 500  | 0.0001        | -               |
| 15.7143 | 550  | 0.0           | -               |
| 17.1429 | 600  | 0.0001        | -               |
| 18.5714 | 650  | 0.0           | -               |
| 20.0    | 700  | 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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