setfit safetensors roberta sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:mini1013/master_domain base_model:finetune:mini1013/master_domain model-index region:us
---
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: JCP 애플 펜슬 1세대 USB-C Apple Pencil 어뎁터 포함 (MQLY3KH/A)  주식회사 제이씨엠컴퍼니
- text: 힐링쉴드 갤럭시탭S9 울트라 ARAG 고화질 저반사 액정보호필름1매 후면1매  (주) 힐링쉴드코리아
- text: 다이아큐브 아이패드 프로 13 M4 (2024) 9H PET 슬림강화유리 깨지지않는 액정보호필름, 간편부착 2매 6H 고투명 방탄 2매
    뷰티코리아(Beauti korea)
- text: 뷰씨 갤럭시탭S6 라이트 10.4인치 강화유리필름(2매) 강화유리필름(2매구성) 주식회사 오토스마트
- text: 갤럭시탭A9 슈페리어 저반사 액정보호필름  (주) 폰트리
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: 0.9694656488549618
      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:** 4 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                                                                                                                                                                                                                                                                                                        |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 3     | <ul><li>'와콤 KP-501E 표준그립펜 인튜어스 프로 펜 와콤펜  에이엠스토어'</li><li>'Apple 애플 펜슬 2세대 미국정품 MU8F2KH/A (3-5일배송)  굿웍스코리아 유한책임회사'</li><li>'교체형 갤탭 볼펜심 펜촉 탭S7 펜슬 S펜 라미 (G428) 블랙 몽실왕자A'</li></ul>                                                                                                                                |
| 0     | <ul><li>'코끼리리빙 아이패드 갤럭시탭S 마그네틱 드로잉 필기 스탠드 거치대 P2WA-3419 12.9(2018/2020/2021/2022)_그레이 주식회사예스대현'</li><li>'뷰씨 갤럭시탭 아이패드 태블릿 거치대 침대 책상 틈새 고정 블랙 주식회사 오토스마트'</li><li>'알파플랜 휴대용 태블릿 거치대 스탠드 갤럭시탭 아이패드 ATH01 매트블랙 주식회사 로리스토어'</li></ul>                                                                               |
| 2     | <ul><li>'뷰씨 아이패드 에어 6세대 11인치 M2 종이 질감 저반사 액정 보호 필름 에어6세대 11인치 (저반사)종이질감필름 제이포레스트'</li><li>'아이패드 에어 6세대 11 종이질감 Light 액정보호필름1매 후면1매  주식회사 스마트'</li><li>'아이패드 프로 3세대 12.9인치 지문방지 종이질감 액정보호필름 아이패드 프로 3세대 12.9_종이질감 액정보호필름 1매 주식회사 제이앤에이'</li></ul>                                                                |
| 1     | <ul><li>'Apple 아이패드 에어 스마트 폴리오 (iPad Air 4,5세대용) - 다크 체리 (MNA43FE/A) 다크 체리 MNA43FE/A (주)블루박스 (Blue Box Co., Ltd)'</li><li>'[N페이적립+커피쿠폰] ESR 아이패드 프로13 폴리오 케이스 프로13_네이비 EC587 주식회사 샘빌'</li><li>'뷰씨 갤럭시탭 S8플러스 / S7플러스 / S7 FE 12.4인치 보디가드 투명범퍼 케이스 갤럭시탭S8+/S7+/S7 FE(공용)_보디가드ㅣ투명 광주스마트폰친구 아이폰 사설수리센터점'</li></ul> |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.9695 |

## 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_el22")
# Run inference
preds = model("갤럭시탭A9 슈페리어 저반사 액정보호필름  (주) 폰트리")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 6   | 12.075 | 34  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 50                    |
| 1     | 50                    |
| 2     | 50                    |
| 3     | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0312  | 1    | 0.4959        | -               |
| 1.5625  | 50   | 0.0683        | -               |
| 3.125   | 100  | 0.0002        | -               |
| 4.6875  | 150  | 0.0001        | -               |
| 6.25    | 200  | 0.0001        | -               |
| 7.8125  | 250  | 0.0           | -               |
| 9.375   | 300  | 0.0           | -               |
| 10.9375 | 350  | 0.0           | -               |
| 12.5    | 400  | 0.0           | -               |
| 14.0625 | 450  | 0.0           | -               |
| 15.625  | 500  | 0.0           | -               |
| 17.1875 | 550  | 0.0           | -               |
| 18.75   | 600  | 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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