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: 옴므 교체용 가죽 벨트끈 벨트줄 허리띠 벨트 가죽 수동 자동용 22_수동벨트용 이태리가죽 3.3cm_카멜(42인치) 에스컴퍼니
- text: 여성 여자 패션 와이드 밴딩 벨트 패딩 코트 허리 허리띠 원피스 가디건 코디 패딩벨트 088_(SH30)_아이보리 {SH30-Ivory}
    스웰swell
- text: '[1 + 1]쭉쭉스판 늘어나는 밴딩 벨트 남여공용 캐쥬얼 데일리 군용 텍티컬 벨트 01. 늘어나는 벨트 1+1_05. 다크브라운_라이트브라운
    스토리몰2'
- text: '[로제이] 정장 캐주얼 가죽 더블 서스펜더 멜빵 NRMGSN011_BL 블랙_free '
- text: 모두샵 남자 가죽 청바지벨트 캐주얼벨트 허리띠 이니셜각인 7. 브라운 D107_한글(정자체)_보통길이(36까지착용가능) 모두샾
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: 0.9649836541954232
      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>'고리 집게 가방 여행용 멜빵 클립 다용도 삼각버클 후크  옐로우몰'</li><li>'패션 여성서스펜더 스트랩 양복 출근룩 정장 코스튬 흰색 폭 2.5cm 120cm 맴매2'</li><li>'패션 여성서스펜더 스트랩 양복 출근룩 정장 코스튬 파란색 흰색 빨간색 줄무늬 폭2.5 120cm 맴매2'</li></ul>                                 |
| 2.0   | <ul><li>'Basic Leather Belt 네이비_100cm 만달문화여행사'</li><li>'다이에나롤랑 러블리 여자벨트 146276 은장 브라운 FCB0012CM_L 105 네잎클로버마켓'</li><li>'[갤러리아] 헤지스핸드백HJBE2F406W2브라운 스티치장식 소가죽 여성 벨트(타임월드)  한화갤러리아(주)'</li></ul>                         |
| 0.0   | <ul><li>'(아크테릭스)(공식판매처)(23SS) 컨베이어 벨트 32mm (AENSUX5577) BLACK_SM '</li><li>'[갤러리아] 헤지스핸드백 HJBE2F775BK_ 블랙 빅로고 버클 가죽 자동벨트(타임월드)  한화갤러리아(주)'</li><li>'닥스_핸드백 (선물포장/쇼핑백동봉) 블랙 체크배색 가죽 자동벨트 DBBE3E990BK  롯데백화점2관'</li></ul> |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.9650 |

## 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_ac3")
# Run inference
preds = model("[로제이] 정장 캐주얼 가죽 더블 서스펜더 멜빵 NRMGSN011_BL 블랙_free ")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 9.6133 | 17  |

| 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    | 0.394         | -               |
| 2.0833  | 50   | 0.0731        | -               |
| 4.1667  | 100  | 0.0           | -               |
| 6.25    | 150  | 0.0           | -               |
| 8.3333  | 200  | 0.0           | -               |
| 10.4167 | 250  | 0.0           | -               |
| 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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