---
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: 한국금거래소 순금 길상무늬 골드바 1g 기본 종이 케이스 주식회사 한국금거래소디지털에셋
- text: '[뽀르띠/부모님선물] 순금 24K 0.5g 카드형 카네이션 골드바 06 존경_화이트 뽀르띠'
- text: 순금 미니골드바 3.75g 각인 메세지 편지 순금선물 24K 999.9 재테크 금투자 3.75g 골드바+메세지 각인+고급케이스 골드베이
- text: 순금뱃지 1.875g 기업 회사 은행 병원 대학교 금뱃지 2.금형추가 투자골드
- text: '[한국표준금거래소] 컷팅 하트 골드바 1g 고급 패키지+쇼핑백O (주)한국표준거래소'
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: 0.9976689976689976
      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                                                                                                                                                                            |
|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0.0   | <ul><li>'[한국표준금거래소] 999.9‰순금 골드바 11.25g 쇼핑백X (주)한국표준거래소'</li><li>'한국금거래소 순금 꽃다발 골드바 0.2g 기본 종이 케이스 한국금거래소디지털에셋'</li><li>'한국금거래소 순금 비상금 통장 골드바 1g  주식회사 한국금거래소디지털에셋'</li></ul>       |
| 1.0   | <ul><li>'[한국금거래소]한국금거래소 순금 복주머니 3.75g  롯데아이몰'</li><li>'[한국금거래소] 어락도 금수저 카드 3.75g  주식회사 한국금거래소디지털에셋'</li><li>'순금거북이 37.5g  종로골드'</li></ul>                                           |
| 2.0   | <ul><li>'[한국금거래소] 실버바 100g 은테크 은투자 은시세 생일 기념일 축하 선물  주식회사 한국금거래소디지털에셋'</li><li>'[100g 실버바] 한국금거래소 99.99% 투자용 은괴  주식회사 골드나라'</li><li>'[삼성금거래소]Silver Bar(실버바)100g  AKmall'</li></ul> |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.9977 |

## 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_ac5")
# Run inference
preds = model("순금뱃지 1.875g 기업 회사 은행 병원 대학교 금뱃지 2.금형추가 투자골드")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 4   | 7.7583 | 17  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 50                    |
| 1.0   | 50                    |
| 2.0   | 20                    |

### 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0526  | 1    | 0.4971        | -               |
| 2.6316  | 50   | 0.0373        | -               |
| 5.2632  | 100  | 0.0001        | -               |
| 7.8947  | 150  | 0.0           | -               |
| 10.5263 | 200  | 0.0           | -               |
| 13.1579 | 250  | 0.0           | -               |
| 15.7895 | 300  | 0.0           | -               |
| 18.4211 | 350  | 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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