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: 이소닉 MR-120 8GB 동의 화이트선셋
- text: 이소닉 PCM-007 2G 무손실 PCM녹음 간단한사용법 볼펜녹음기 고성능 인터뷰 회의녹음/강의녹음/비밀녹음/녹취기/전화녹음 증거보존
    초소형녹음기+USB메모리+MP3 원거리녹음 2GB 진경전자
- text: ICD-PX470 4GB 속기사녹음기 비밀녹음기 장시간  동백
- text: 베스타 전자사전 BK-100  핫앤쿨 (HNC)
- text: TASCAM 4트랙 디지털 오디오 레코더 DR-40X  고운소리사
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: 0.9616204690831557
      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>'레벤트 백색소음기 무드등 RM20  (주)포트리스'</li><li>'레벤트 RM20 LED램프 무드등 백색소음기 수면등  (주)엠글로벌스'</li><li>'레벤트 신생아 백색소음기 화이트노이즈 수유 무드등 RM20  주식회사매니악'</li></ul>                                                                                                       |
| 1     | <ul><li>'아이플라이텍 스캔톡 영어 번역기 동시 통역기 AI 인공지능 AI-DSA-001  펫 허브'</li><li>'아이플라이텍 스캔톡 영어 번역기 통역기 AI 인공지능 어학기 (3종 액세사리 무료 ) 그린_액정보호필름/젤리케이스/하드케이스 주식회사 엑스오비'</li><li>'슈피겐 인공지능 음성 번역기 동시 통역기 포켓토크 영어 일본어 중국어 베트남어 해외여행 화이트(eSIM) /ESE00001 주식회사 슈피겐코리아'</li></ul> |
| 0     | <ul><li>'브리츠 BZ-VR1000 보이스 레코드 회의 강의 녹음  주식회사 투데이플러스'</li><li>'머레이 손목 시계형 보이스 레코더 RV-1000  알앤컴퍼니 (R&Company)'</li><li>'이소닉 MR-1000 8GB 장시간 특수녹음기 초소형 보이스레코더 증거 대화 강의  (주)포고텍'</li></ul>                                                                    |
| 2     | <ul><li>'베스타 한자 일본어 국어 중국어 어학 영영 한영 옥편 영어 사전 전자사전 BK-100  (주)삼신이앤비'</li><li>'베스타 BK-200J 전자사전 일본어특화 전자사전 필기인식 메모리+액정필름+아답타  주식회사 마루엔'</li><li>'베스타 BK-200 8GB 전자사전 번역 회화  주식회사 모핏코리아'</li></ul>                                                            |
| 3     | <ul><li>'교보문고 sam 10 Plus  셜크'</li><li>'교보 이북리더기 샘10플러스 sam 10 Plus  프린지'</li><li>'리디페이퍼 4 RIDIPAPER 4 리디 전용 전자책 이북리더기 전자책 리더기 (7인치, wifi, 블루투스, 방수) 블랙 리디 주식회사'</li></ul>                                                                                 |
| 4     | <ul><li>'스코코 리디북스 리디페이퍼 4세대 무광 전신 외부보호필름 3종  (주)스코코'</li><li>'세이펜전용충전기 / 5핀 C타입 분리형충전기 세이펜5핀충전기 (주)세티'</li><li>'스캔톡 EPU 액정 보호 필름 2매입 2매입(벌크용) 주식회사 포유컴퍼니'</li></ul>                                                                                        |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.9616 |

## 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_el23")
# Run inference
preds = model("이소닉 MR-120 8GB 동의 화이트선셋")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 3   | 10.4144 | 23  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 50                    |
| 1     | 42                    |
| 2     | 50                    |
| 3     | 11                    |
| 4     | 12                    |
| 5     | 16                    |

### 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    | 0.4957        | -               |
| 1.7241  | 50   | 0.0279        | -               |
| 3.4483  | 100  | 0.0001        | -               |
| 5.1724  | 150  | 0.0001        | -               |
| 6.8966  | 200  | 0.0001        | -               |
| 8.6207  | 250  | 0.0001        | -               |
| 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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