---
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: 천주교 세례명각인 5단 매듭묵주팔찌 이레네오 라일락_어린이 루아(RUAH)
- text: 손가락염주 불교 단주 미니 염주 합장주 악세사리 108 108배 선택4.라일락 스테디오더
- text: 손가락염주 벽조목 미니염주 건강 불교용품 경면주사 V 이커머스히어로
- text: 손가락염주 벽조목 미니염주 건강 불교용품 경면주사 E 이커머스히어로
- text: 제사 불전 부처님 신당 제단 법당 향로 그릇 향받침대 클리어런스퍼니스스타일랜덤헤어1 복실이총각
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: 0.8396946564885496
      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>'태국침향 티베트 인센스 디퓨저 천연 향초 향로 24개  트러스트(trust)쇼핑몰'</li><li>'엘캔들x보리심양초 밀대 원백 돈타래 쌍대 1박스 돈타래 1박스 40개입 엘캔들'</li><li>'수인당천무 소원부적 스티커 13종 관재구설부 도깨비몰'</li></ul>          |
| 2.0   | <ul><li>'가톨릭 성화 성인 원형 성수병 30ml 주문제작  메리블라썸'</li><li>'티베트 야크 본 비즈 108 말라 묵주 기도문 목걸이 10mm white 뮤니샵'</li><li>'과달루페 성모상 팔찌 목걸이 마리아 가톨릭 실버 스털링  엠에스(MS)쇼핑'</li></ul>         |
| 0.0   | <ul><li>'주문제작- 어린이 전도지 (명함9x5초청장)-500장 1번_1000 자라나는 씨'</li><li>'입교증서 우단증서 A5 KJ 상장케이스 상장용지 교회용품  부광'</li><li>'주문제작- 어린이 전도지 (명함9x5초청장)-500장 3번-하늘색_500 자라나는 씨'</li></ul> |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.8397 |

## 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_lh23")
# Run inference
preds = model("손가락염주 벽조목 미니염주 건강 불교용품 경면주사 V 이커머스히어로")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 9.3867 | 22  |

| 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    | 0.4271        | -               |
| 2.0833  | 50   | 0.029         | -               |
| 4.1667  | 100  | 0.0007        | -               |
| 6.25    | 150  | 0.0002        | -               |
| 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}
}
```

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