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: SD 바이오 에스디 코드프리 당뇨검사지 4박스 200매 (유효기간 2025년 03월) 코드프리 200매+알콜솜 100매 엠에스메디칼
- text: 아큐첵 소프트클릭스 채혈기+채혈침 25개 액티브 퍼포마 인스턴트 가이드 란셋 채혈바늘  주식회사 더에스지엠
- text: 녹십자 혈당시험지 당뇨 시험지 그린닥터 50매 시험지100매+체혈침100개 자재스토어
- text: HL 지닥터 혈당시험지 100매 /당뇨측정 검사지 스트립 1_지닥터 혈당시험지 100매+알콜솜100매 헬스라e프
- text: 비디 울트라파인 인슐린 주사기 1박스 100개 328821[31G 8mm 0.5ml]BD 펜니들 주사바늘 울트라파인2 BD 인슐린 31G
    6mm 0.5ml 1박스(324901) 더메디칼샵
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: 0.9786747905559787
      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:** 3 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                                                                                                                                                                                                                                                                                        |
|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1.0   | <ul><li>'프리스타일 리브레 무채혈 연속혈당측정기(24년1월)얼라이브패치1매 거래명세서 광명헬스케어'</li><li>'SD 코드프리 혈당측정기(측정기+채혈기+침10매+파우치)P  스토어알파'</li><li>'올메디쿠스 글루코닥터 탑 혈당계 AGM-4100+파우치+채혈기+채혈침 10개  엠에스메디칼'</li></ul>                                                                                                            |
| 2.0   | <ul><li>'에스디 SD 코드프리 측정지|검사지|시험지 100매(25년 2월)  더메디칼샵'</li><li>'바로잰 당뇨검사 혈당시험지 100매(50매x2팩) 사용기한 25년 3월 MinSellAmount 유니프라이스'</li><li>'옵티엄 프리스타일 케톤시험지1박스10매 검사지 혈중 (24년 8월)  메디트리'</li></ul>                                                                                                    |
| 0.0   | <ul><li>'비디 울트라파인 인슐린 주사기 1박스 100입 324901 [31G 6mm 0.5ml] BD 펜니들 주사바늘 울트라파인2 BD 인슐린 31G 8mm 3/10ml(0.5단위) 1박스(320440) 더메디칼샵'</li><li>'BD 비디 울트라파인 인슐린 주사기 시린지 31G 6mm 1ml 324903 100입  주식회사 더에스지엠'</li><li>'정림 멸균 일회용 주사기 3cc 23g 25mm 100개입 멸균주사기 10cc 18G 38mm(100ea/pck) (주)케이디상사'</li></ul> |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.9787 |

## 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_lh7")
# Run inference
preds = model("녹십자 혈당시험지 당뇨 시험지 그린닥터 50매 시험지100매+체혈침100개 자재스토어")
```

<!--
### Downstream Use

*List how someone could finetune this model on their own dataset.*
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 4   | 9.62   | 21  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 50                    |
| 1.0   | 50                    |
| 2.0   | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0417  | 1    | 0.4565        | -               |
| 2.0833  | 50   | 0.1836        | -               |
| 4.1667  | 100  | 0.1645        | -               |
| 6.25    | 150  | 0.0004        | -               |
| 8.3333  | 200  | 0.0001        | -               |
| 10.4167 | 250  | 0.0001        | -               |
| 12.5    | 300  | 0.0           | -               |
| 14.5833 | 350  | 0.0           | -               |
| 16.6667 | 400  | 0.0           | -               |
| 18.75   | 450  | 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->