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:
- metric
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: LG전자 올레드 TV OLED55C2FNA 스탠드 윤성 운송료상이  윤성종합가전
- text: '[엡손] EH-LS500W / 4K UHD 4000안시 2,500,000:1 EPSON 빔 프로젝터 초단초점  (주)메리트정보'
- text: 루컴즈 2024년형 50인치 스마트 UHD 구글 TV 4K 에너지효율 1등급 T5003KUG 스탠드  빌리어네어디
- text: 이노스 S8601KU LG 패널 스마트 TV 구글티비 벽걸이 기사방문설치(브라켓별도)_수도권(서울경기인천)_86인치 QLED 구글TV
    (주)티지디지털
- text: 삼성 WMN4070SG 벽결이브라켓 삼성고정브라켓  두루엠에스
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: 0.763001415762152
      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:** 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     | <ul><li>'윤씨네 J-SV / 족자스크린 4:3비율 100인치 에스앤피'</li><li>'[ FLAT FLOW ] 플랏플로우 100인치 와이드 분리형 족자스크린 F-HJ100W F-HJ100W (100인치 와이드 족자형) 아이티원'</li><li>'윤씨네 J-SH40 / 와이드 족자스크린 16:9 40인치  에스앤피'</li></ul>                                                                                                                                                                      |
| 2     | <ul><li>'75인치 189cm 4K UHD 비즈니스TV LH75BECH 스탠드 에너지효율등급 1등급 우수한 내구성  주식회사 쇼핑하는니체'</li><li>'[LG] 55인치 UHD 단독형 사이니지 3시리즈 (55UL3J) 고정형 벽걸이 설치 주식회사 케이엠시스템'</li><li>'[LG] 55인치 비디오월 슬림 베젤 1.74 mm, 500nit (55VM5J) 벽걸이 설치 (별도문의) 주식회사 케이엠시스템'</li></ul>                                                                                                                   |
| 5     | <ul><li>'벤큐 GS50 풀HD 캠핑용 빔프로젝터 안드로이드 아이폰 무선미러링 배터리내장 블루투스  (주)아솔컴퍼니'</li><li>'에이서 DX227 🧡정품 신형🧡 5200안시 XGA 20000:1 DLP 회의용 교육용 강당용 멀티용  도움에이브이'</li><li>'[피제이시스] 엡손 EB-L1070U 레이저프로젝터 ❤️정품새상품 ❤️  주식회사 피제이시스(PJSYS.co.Ltd.)'</li></ul>                                                                                                                               |
| 0     | <ul><li>'이노스 S2401KU  어반스톡'</li><li>'[무결점] 프리즘 바이런 75인치 1등급 4K HDR 베젤리스TV 패널 2년 무상보증 / BR750UD_기사설치포함  (주)프리즘코리아'</li><li>'[무결점] 프리즘 바이런 55인치 1등급 4K HDR 베젤리스TV 패널 2년 무상보증 / BR550UHD  (주)프리즘코리아'</li></ul>                                                                                                                                                          |
| 4     | <ul><li>'[PICO 국내 공식판매처] PICO NEO3 Enterprise VR (256GB) / 공공기관 및 공공교육기관 전용  주식회사 메타에듀시스'</li><li>'에듀플레이어 EA400 DVD플레이어 CD/DVD리핑 투웨이 블루투스 EA400 (ED404) 주식회사 에듀플레이어'</li><li>'오큘러스 퀘스트2 Oculus Quest2 올인원 VR게임헤드셋 퀘스트2 128GB (관세 대납) 팽마켓'</li></ul>                                                                                                                  |
| 1     | <ul><li>'카멜 디지털액자화이트(블랙) / PF1040IPS /10인치 디지털액자(동영상,슬리이드쇼,앨범) 선물용디지털액자PF-1040IPS / 디지털사진액자/ 16:9화면(화이트or 블랙) 블랙 에스라B2B'</li><li>'컴스마트 BM170 15.4형 스마트 디지털 액자 동영상 시계 달력 HDMI 서브 모니터  블루시스템쇼핑몰 주식회사'</li><li>'카멜 디지털액자 10인치 PF-1040IPS 미니모니터 사진 동영상 음악  에스제이인터내셔널'</li></ul>                                                                                          |
| 3     | <ul><li>'COMBO-2000A (금영 (KY)/ 내셔널 (NATIONAL) / 넥스디지탈 (NEX) /넥슨 (NEXN) /뉴썬인더스트리 엔플러스(NPLUS)/ 다비디스플레이 (DAVI) COMBO-2000A 메카트로주식회사'</li><li>'COMBO-119 /APH13000/AP-H3020/AP-H4000/APH-H2300/AP-HH232N/IAS-T1010/IAS-T810/IAS-T82CA  지에이치스토어'</li><li>'COMBO-2201 (AKB75455603 / AKB75635301 / AKB75635305 / AKB75675304 / akb75675306 / AKB75755301)  메카트로'</li></ul> |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.7630 |

## 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_el13")
# Run inference
preds = model("삼성 WMN4070SG 벽결이브라켓 삼성고정브라켓  두루엠에스")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 3   | 10.4229 | 25  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 50                    |
| 1     | 50                    |
| 2     | 50                    |
| 3     | 50                    |
| 4     | 50                    |
| 5     | 50                    |
| 6     | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0182  | 1    | 0.4965        | -               |
| 0.9091  | 50   | 0.118         | -               |
| 1.8182  | 100  | 0.0382        | -               |
| 2.7273  | 150  | 0.0008        | -               |
| 3.6364  | 200  | 0.0003        | -               |
| 4.5455  | 250  | 0.0002        | -               |
| 5.4545  | 300  | 0.0002        | -               |
| 6.3636  | 350  | 0.0002        | -               |
| 7.2727  | 400  | 0.0001        | -               |
| 8.1818  | 450  | 0.0001        | -               |
| 9.0909  | 500  | 0.0001        | -               |
| 10.0    | 550  | 0.0001        | -               |
| 10.9091 | 600  | 0.0001        | -               |
| 11.8182 | 650  | 0.0001        | -               |
| 12.7273 | 700  | 0.0001        | -               |
| 13.6364 | 750  | 0.0001        | -               |
| 14.5455 | 800  | 0.0001        | -               |
| 15.4545 | 850  | 0.0001        | -               |
| 16.3636 | 900  | 0.0001        | -               |
| 17.2727 | 950  | 0.0001        | -               |
| 18.1818 | 1000 | 0.0001        | -               |
| 19.0909 | 1050 | 0.0001        | -               |
| 20.0    | 1100 | 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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