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---
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: 한글과컴퓨터 한컴오피스 2024 한글 Open 라이선스 [기업용/영구/2User이상] 한컴오피스 2024 (한글/한셀/한쇼) (주)유비소프트웨어
- text: 한글과컴퓨터 한글 2022 (기업용/패키지/USB방식)  아이코다(주)
- text: 한글과컴퓨터 한컴독스 기업용 ESD 1년 사용  (주)대성클라우드
- text: '[한글과컴퓨터] 한컴오피스 2022 [기업용/패키지/1년사용/제품키배송형]  (주)컴퓨존'
- text: '[마이크로소프트코리아] MS Windows 7 Professional DSP 한글 64bit/정품라벨  (주)소프트존'
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: 1.0
      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                                                                                                                                                                                                                                                                                                                |
|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 4     | <ul><li>'정품 스토어 MS Windows 11 Home 한글 FPP 윈도우11 홈 설치USB 패키지 인증키  (주)에스비코어'</li><li>'윈도우11 프로 FPP(USB) 노트북 업그레이드 전용상품  주식회사 이좋은세상'</li><li>'[MS코리아정품] Windows 11 Pro FPP 한글 처음사용자용 영구 제품키  주식회사 레오솔루션'</li></ul>                                                                                                         |
| 1     | <ul><li>'[Adobe] Photoshop for teams [기업용/라이선스/1년사용] [1개~9개 구매시(1개당 가격)] [발송 3~7일 소요] 갱신 (주)컴퓨존'</li><li>'Movavi Video Editor 2024 기업용 라이선스 / 모바비  주식회사 글래드소프트'</li><li>'Movavi Video Suite 2024 공공기관용 라이선스 / 모바비2024  메모리콕'</li></ul>                                                                                  |
| 2     | <ul><li>'안랩 V3 Net for Windows Server 9.0 DSP (1년)  (주)위프로소프트'</li><li>'안랩 V3 Net for Windows Server 9.0 (기업용/DSP/1년)  아이코다(주)'</li><li>'안랩 V3 Net for Unix Server (기업용 1년사용)  아이코다(주)'</li></ul>                                                                                                                       |
| 3     | <ul><li>'[문자발송]한컴독스 개인용 1년(구독형 한컴오피스) / 윈도우 맥용 설치 파일 지원  주식회사 지엘스토어'</li><li>'한컴독스 개인용 1년 제품키배송형(구독형 한컴오피스) / 윈도우 맥용 설치 파일 지원 확인 주식회사 라이프큐브'</li><li>'[마이크로소프트] Office 2019 Home & Student PKC [가정용/패키지/한글] 택배 발송  오시리스랩 주식회사'</li></ul>                                                                                |
| 5     | <ul><li>'[1분발송]리훈 오늘기억 일기장 다이어리 굿노트 아이패드 PDF 속지 3년 감사 1.오른손잡이용_1.3년다이어리 주식회사 리훈 (RIHOON CO., LTD.)'</li><li>'[스티커2종] 24년 오리지날 굿노트 디지털 속지 - 데일리 가로형(1D2P 형식) (아이패드 갤럭시탭 하이퍼링크 PDF 속지)  (주)프랭클린 플래너 코리아'</li><li>'[1분발송]리훈 하고싶은말 일기장 다이어리 굿노트 아이패드 PDF 속지 날짜형（23년10월-24년12월）_오른손잡이용 주식회사 리훈 (RIHOON CO., LTD.)'</li></ul> |
| 0     | <ul><li>'Radmin 3 Standard license 기업용/ 영구(ESD)  (주)삼경엠'</li><li>'Radmin 3 - 50 Licenses Pack 기업용 라이선스 /알어드민 / 원격지원 / 50대설치  메모리콕'</li><li>'Radmin 3 Standard 기업용 라이선스 /알어드민 / 원격지원  메모리콕'</li></ul>                                                                                                                    |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 1.0    |

## 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_el12")
# Run inference
preds = model("한글과컴퓨터 한컴독스 기업용 ESD 1년 사용  (주)대성클라우드")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 6   | 11.8852 | 21  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 3                     |
| 1     | 34                    |
| 2     | 33                    |
| 3     | 50                    |
| 4     | 50                    |
| 5     | 13                    |

### 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0345  | 1    | 0.496         | -               |
| 1.7241  | 50   | 0.0031        | -               |
| 3.4483  | 100  | 0.0001        | -               |
| 5.1724  | 150  | 0.0           | -               |
| 6.8966  | 200  | 0.0           | -               |
| 8.6207  | 250  | 0.0           | -               |
| 10.3448 | 300  | 0.0           | -               |
| 12.0690 | 350  | 0.0           | -               |
| 13.7931 | 400  | 0.0           | -               |
| 15.5172 | 450  | 0.0           | -               |
| 17.2414 | 500  | 0.0           | -               |
| 18.9655 | 550  | 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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