---
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: 삼성 노트북 NT450R5E K81S K82P K82W K83S K85S 정품 어댑터 아답터 아답타 충전기 AD-6019R 19V 3.16A  뉴
    스마트 전자
- text: 인트존 205X 노트북 파우치 13인치 15인치 핸디 가방 13인치_스모키블랙 크로니시스템
- text: 엑토(ACTTO) NBL-04 노트북 도난방지 케이블/(화이트)  국진컴퓨터
- text: 삼성 정품어댑터AD-4019A/19V2.1A/NT930X5J-K82S/4019P  엔티와이
- text: LG 그램 17Z90SP & 17ZD90SP 17인치 퓨어 저반사 지문방지 액정보호필름  제트비컴퍼니
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: 0.9272844272844273
      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:** 9 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                                                                                                                                                                                                                                           |
|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 8     | <ul><li>'MSI 프레스티지 16 AI Evo B1MG 노트북 키스킨 커버 무소음 키보드  올유어리브'</li><li>'맥북 에어 15인치 키스킨 M2 실리콘 키보드덮개  (주)스코코'</li><li>'삼성갤럭시북3 Go 키스킨 NT345XPA-KC04S 키스킨 키커버 14인치 실리스킨 문자인쇄 키스킨(블랙) 에이플'</li></ul>                                                    |
| 0     | <ul><li>'칼디짓 엘레멘트독 CalDigit Element Dock 썬더볼트4 독 멀티허브 맥북프로 Element Dock (주)디엔에이치'</li><li>'마하링크 2.5인치 SATA 멀티부스트 ML-MBS127  디메이드 (DMADE)'</li><li>'AA-AE2N12B usb 젠더 컴퓨터 인터넷 설치 랜 포트  에스아이'</li></ul>                                              |
| 3     | <ul><li>'잘만 ZM-NS1000 정품/노트북 받침대/쿨링패드  주식회사보성닷컴'</li><li>'-잘만 ZM-NS1 (블랙)-  주식회사 케이에이치몰'</li><li>'잘만 노트북 쿨링 받침대 ZM-NS2000  (주)아싸컴'</li></ul>                                                                                                       |
| 5     | <ul><li>'W01 HP Omen 17-ANxxxTX 시리즈용 Crystal액정보호필름  더블유공일'</li><li>'맥북 에어 15인치 필름 M2 무광 하판 외부 1매 무광 상판 1매 (주)스코코'</li><li>'맥북에어 M3 2024 15인치 외부보호필름 3종세트  에이엠스토어'</li></ul>                                                                        |
| 1     | <ul><li>'이지엘 국산 가벼운 손잡이 노트북 파우치 케이스 13.3인치 For 13.3인치_스모키블랙 이지엘'</li><li>'[에버키] Titan 타이탄 EKP120 18.4인치  비투비마스터'</li><li>'LG 그램 14인치 전용 가죽 파우치  (주) 티앤티정보 용산전자랜드지점'</li></ul>                                                                      |
| 6     | <ul><li>'[프라임디렉트] 아답터, 220V / 19V 3.42A [내경2.1~2.5mm/외경5.5mm] 전원 케이블 미포함 [비닐포장]  (주)컴퓨존'</li><li>'삼성 정품 노트북 NT-RV720 / 19V 3.16A AD-6019S AD-6019R 정품 전원 어댑터  고다'</li><li>'EFM ipTIME 어댑터 48V-0.5A (ipTIME 제품군 호환용) [ 아이피타임 ]  (주)클럽라이더'</li></ul> |
| 7     | <ul><li>'HP 노트북배터리 14 15 TPN-Q207 Q208 HT03XL 호환용배터리  라온하람몰'</li><li>'(AA-PB9NC6B)삼성 정품 노트북 배터리/NT-RF410 RF411 RF510 RF511 RF710 RF711 전용  엔티와이'</li><li>'삼성 정품 배터리 AA-PB9NC6B/NT-R530 R540 전용 노트북 배터리/ NTY  엔티와이'</li></ul>                       |
| 2     | <ul><li>'강원전자 넷메이트 노트북 도난방지 USB포트 와이어 잠금장치 키 타입 NM-SLL05M  보다넷'</li><li>'노트북 도난방지 와이어 잠금장치 NM-SLL03  주식회사 루피하루'</li><li>'엑토(ACTTO) NBL-01 노트북 도난방지 케이블/잠금장치  국진컴퓨터'</li></ul>                                                                      |
| 4     | <ul><li>'ASUS 비보북 15 X1504ZA 노트북보안필름 프라이버시 사생활보호 거치형 거치형보안필름_1장 한성'</li><li>'[1300K] HP 빅터스 16-SxxxxAN 거치식 양면 사생활보호필터F  엔에이치엔위투 주식회사'</li><li>'삼성전자 갤럭시북4 NT750XGL-XC51S 노트북보안필름 프라이버시 사생활보호 부착형 부착형보안필름_1장 원일'</li></ul>                          |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.9273 |

## 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_el7")
# Run inference
preds = model("엑토(ACTTO) NBL-04 노트북 도난방지 케이블/(화이트)  국진컴퓨터")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 4   | 10.3626 | 23  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 50                    |
| 1     | 50                    |
| 2     | 50                    |
| 3     | 50                    |
| 4     | 22                    |
| 5     | 50                    |
| 6     | 50                    |
| 7     | 50                    |
| 8     | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0152  | 1    | 0.4966        | -               |
| 0.7576  | 50   | 0.184         | -               |
| 1.5152  | 100  | 0.037         | -               |
| 2.2727  | 150  | 0.0256        | -               |
| 3.0303  | 200  | 0.0014        | -               |
| 3.7879  | 250  | 0.0002        | -               |
| 4.5455  | 300  | 0.0006        | -               |
| 5.3030  | 350  | 0.0001        | -               |
| 6.0606  | 400  | 0.0001        | -               |
| 6.8182  | 450  | 0.0001        | -               |
| 7.5758  | 500  | 0.0001        | -               |
| 8.3333  | 550  | 0.0001        | -               |
| 9.0909  | 600  | 0.0001        | -               |
| 9.8485  | 650  | 0.0001        | -               |
| 10.6061 | 700  | 0.0001        | -               |
| 11.3636 | 750  | 0.0001        | -               |
| 12.1212 | 800  | 0.0001        | -               |
| 12.8788 | 850  | 0.0001        | -               |
| 13.6364 | 900  | 0.0001        | -               |
| 14.3939 | 950  | 0.0001        | -               |
| 15.1515 | 1000 | 0.0001        | -               |
| 15.9091 | 1050 | 0.0001        | -               |
| 16.6667 | 1100 | 0.0001        | -               |
| 17.4242 | 1150 | 0.0           | -               |
| 18.1818 | 1200 | 0.0           | -               |
| 18.9394 | 1250 | 0.0           | -               |
| 19.6970 | 1300 | 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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