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: '[저소음 미세입자] 오므론 네블라이저 NE-C803  꿈꾸는약국'
- text: 일동제약 케어리브 밴드 M 중형 10매입 약국용 3_중형 M 50매 이웃사랑팜
- text: 퀸사이즈 병원침대/환자용침대 매트리스/고탄성 병원용 접이식 마사지 지압 의료용 매트 두께 7cm_베이지색 평매트리스_1400mm X
    2000mm(더블사이즈) 메디칼베드마트
- text: 일동제약 케어리브 밴드 중형 M 50매입 하이맘(중외제약)_하이맘밴드 아쿠아 혼합형 12매 테크노 제일약국
- text: '[하프클럽/제일케어]웰팜스 의료기기 - 의료용 가위 1개  하프클럽'
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: 0.9570833333333333
      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:** 5 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                                                                                                                                                                                                                       |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 2.0   | <ul><li>'세운 네라톤카테타 #1116 라텍스 멸균 100개 팩 6번 12fr 4.0mm0  트리비즈니스'</li><li>'세운 바로박(Barovac) PS200C 단위:1개  (주)엠디오씨'</li><li>'의무실 성인용 고무밴드 네블라이저 마스크 호흡기 흡입마스크 기관지  인사이트쇼핑몰'</li></ul>                                               |
| 1.0   | <ul><li>'JW중외제약 하이맘밴드 프리미엄 2매 이지덤(대웅제약)_이지덤씬 2매(+가위) 테크노 제일약국'</li><li>'메디폼 친수성 폼드레싱 10x10cm (5mm) (2mm) 10매입 1박스 5mm 주식회사 엠퍼러'</li><li>'메나리니 더마틱스 울트라 겔 15g 1개.  릴리뷰티'</li></ul>                                              |
| 0.0   | <ul><li>'약국 에탄올스왑 일회용 알콜솜 에프에이 이올스왑 알콜스왑 소독솜 1박스  다팜메디'</li><li>'[유한양행] 해피홈 소독용 알콜스왑알콜솜 100매입 2개 [0001]기본상품 CJONSTYLE'</li><li>'일회용 알콜솜 알콜스왑 소독 약국 바른케어 개별포장100매 바른케어 플러스 알콜솜 100매 로그엠(LOGM)'</li></ul>                        |
| 4.0   | <ul><li>'가주 비멸균 설압자 1통(100개) 혀누르개 목설압자 의료용 병원용 더블세이프 MinSellAmount 이원헬스케어'</li><li>'의료용 겸자 12.5cm /곡 모스키토 켈리 포셉  SJ헬스케어'</li><li>'개부밧드6절(뚜껑있는밧드)소독통/개무밧드/사각트레이/트레이밧드/거어즈캔  신동방메디칼'</li></ul>                                   |
| 3.0   | <ul><li>'일회용 베드 위생시트 부직포시트 침대커버 1롤 50장 80x180cm 비방수(고급형) 80x180 50장/롤 심비오시스'</li><li>'부직포자루,육수보자기,다시백,거름망 45x50-300장 봉제  지우씨'</li><li>'병원침대/환자용침대 매트리스/고탄성 접이식 마사지 지압 의료용 매트 두께 9cm_밤색 평매트리스_900mm X 1900mm 메디칼베드마트'</li></ul> |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.9571 |

## 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_lh19")
# Run inference
preds = model("[저소음 미세입자] 오므론 네블라이저 NE-C803  꿈꾸는약국")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 10.084 | 20  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 50                    |
| 1.0   | 50                    |
| 2.0   | 50                    |
| 3.0   | 50                    |
| 4.0   | 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 |
|:-----:|:----:|:-------------:|:---------------:|
| 0.025 | 1    | 0.4162        | -               |
| 1.25  | 50   | 0.2435        | -               |
| 2.5   | 100  | 0.0066        | -               |
| 3.75  | 150  | 0.0054        | -               |
| 5.0   | 200  | 0.0001        | -               |
| 6.25  | 250  | 0.0           | -               |
| 7.5   | 300  | 0.0           | -               |
| 8.75  | 350  | 0.0           | -               |
| 10.0  | 400  | 0.0           | -               |
| 11.25 | 450  | 0.0           | -               |
| 12.5  | 500  | 0.0           | -               |
| 13.75 | 550  | 0.0           | -               |
| 15.0  | 600  | 0.0           | -               |
| 16.25 | 650  | 0.0           | -               |
| 17.5  | 700  | 0.0           | -               |
| 18.75 | 750  | 0.0           | -               |
| 20.0  | 800  | 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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