---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 내셔널지오그래픽 NATIONALGEOGRAPHIC 여성 코스토니 플리스 뽀글이 후드 풀집업 N224WFJ910 스포츠/레저>등산>등산의류>재킷
- text: JQS EIDER 레인코트 DUA23917P1 스포츠/레저>등산>등산의류>재킷
- text: 풍수나침반 소형 고정밀 지리 나침판 도구 교육용 풍수소품 전문가용 수맥 측정 휴대용 나반 스포츠/레저>등산>등산장비>나침반
- text: 아이더 국내정품 EIDER 공용 플리스 이너장갑 DUW22V08Z1 1179873 스포츠/레저>등산>등산잡화>장갑
- text: 코오롱스포츠 남녀공용 경량 브림 버킷햇 QERFX22680WIN 스포츠/레저>등산>등산잡화>모자
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: mini1013/master_domain
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: accuracy
      value: 1.0
      name: Accuracy
---

# 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:** 6 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                                                                                                                                                                                                         |
|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 2.0   | <ul><li>'디스커버리익스페디션 여성 제임스쿡 자켓 바람막이 DWWJ34024 스포츠/레저>등산>등산의류>재킷'</li><li>'아이더 버서틀 여성 슬림 구스다운 자켓 여자경량패딩 DWP22541 스포츠/레저>등산>등산의류>점퍼'</li><li>'블랙야크 남여공용 YAK ON-H 하이로프트 플리스 1BYVSW1006 스포츠/레저>등산>등산의류>조끼'</li></ul> |
| 0.0   | <ul><li>'클라이밍 클라이밍초크 등반 카라비너 모양 안전 배낭레인커버 하강기 스포츠/레저>등산>기타등산장비'</li><li>'17 노스페이스 반다나 페이즐리 80 S NA5BQ02D 스포츠/레저>등산>기타등산장비'</li><li>'암벽등반 등강기 실내 클라이밍 도르래 장비 하강기 스포츠/레저>등산>기타등산장비'</li></ul>                      |
| 5.0   | <ul><li>'클라이밍화 초보자 입문용 남성 여성 암벽화 신발 스포츠/레저>등산>등산화'</li><li>'HOKA 남성 카하 2 로우 고어텍스 - 1123190-BBLC 스포츠/레저>등산>등산화'</li><li>'코오롱스포츠 TRAIL 남녀공용 트레일러닝 슈즈 TL-1 FE4TX24010YEX 스포츠/레저>등산>등산화'</li></ul>                   |
| 4.0   | <ul><li>'히키스 카라비너 가방걸이 S 스포츠/레저>등산>등산장비>카라비너'</li><li>'살로몬 Salomon 트레일 게이터 M 7 5-9 스포츠/레저>등산>등산장비>스패츠'</li><li>'알로코리아 WM501 스포츠/레저>등산>등산장비>손난로'</li></ul>                                                        |
| 3.0   | <ul><li>'K2 Safety 메쉬 햇모자 IUS20931 스포츠/레저>등산>등산잡화>모자'</li><li>'MILLET 밀레 남성 봄여름 등산 M 간절기 숏장갑 MXTUL004 스포츠/레저>등산>등산잡화>장갑'</li><li>'살로몬 등산양말 365 크루 FBCSB SO LC2085200 761896 스포츠/레저>등산>등산잡화>양말'</li></ul>         |
| 1.0   | <ul><li>'노스페이스 브리즈 힙색 NN2HP01 스포츠/레저>등산>등산가방'</li><li>'아크테릭스 ARC TERYX 맨티스 16 백팩 가방 MANTIS BACKPACK ABOSUX6136BSR 스포츠/레저>등산>등산가방'</li><li>'블랙야크 BAC 어스유 등산가방 2BYKSX1904 36L 스포츠/레저>등산>등산가방'</li></ul>            |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 1.0      |

## 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_sl8")
# Run inference
preds = model("JQS EIDER 레인코트 DUA23917P1 스포츠/레저>등산>등산의류>재킷")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 8.5167 | 19  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 70                    |
| 1.0   | 70                    |
| 2.0   | 70                    |
| 3.0   | 70                    |
| 4.0   | 70                    |
| 5.0   | 70                    |

### Training Hyperparameters
- batch_size: (256, 256)
- num_epochs: (30, 30)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 50
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch   | Step | Training Loss | Validation Loss |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0120  | 1    | 0.4926        | -               |
| 0.6024  | 50   | 0.4841        | -               |
| 1.2048  | 100  | 0.1569        | -               |
| 1.8072  | 150  | 0.0001        | -               |
| 2.4096  | 200  | 0.0           | -               |
| 3.0120  | 250  | 0.0           | -               |
| 3.6145  | 300  | 0.0           | -               |
| 4.2169  | 350  | 0.0           | -               |
| 4.8193  | 400  | 0.0           | -               |
| 5.4217  | 450  | 0.0           | -               |
| 6.0241  | 500  | 0.0           | -               |
| 6.6265  | 550  | 0.0           | -               |
| 7.2289  | 600  | 0.0           | -               |
| 7.8313  | 650  | 0.0           | -               |
| 8.4337  | 700  | 0.0           | -               |
| 9.0361  | 750  | 0.0           | -               |
| 9.6386  | 800  | 0.0           | -               |
| 10.2410 | 850  | 0.0           | -               |
| 10.8434 | 900  | 0.0           | -               |
| 11.4458 | 950  | 0.0           | -               |
| 12.0482 | 1000 | 0.0           | -               |
| 12.6506 | 1050 | 0.0           | -               |
| 13.2530 | 1100 | 0.0           | -               |
| 13.8554 | 1150 | 0.0           | -               |
| 14.4578 | 1200 | 0.0           | -               |
| 15.0602 | 1250 | 0.0           | -               |
| 15.6627 | 1300 | 0.0           | -               |
| 16.2651 | 1350 | 0.0           | -               |
| 16.8675 | 1400 | 0.0           | -               |
| 17.4699 | 1450 | 0.0           | -               |
| 18.0723 | 1500 | 0.0           | -               |
| 18.6747 | 1550 | 0.0           | -               |
| 19.2771 | 1600 | 0.0           | -               |
| 19.8795 | 1650 | 0.0           | -               |
| 20.4819 | 1700 | 0.0           | -               |
| 21.0843 | 1750 | 0.0           | -               |
| 21.6867 | 1800 | 0.0           | -               |
| 22.2892 | 1850 | 0.0           | -               |
| 22.8916 | 1900 | 0.0           | -               |
| 23.4940 | 1950 | 0.0           | -               |
| 24.0964 | 2000 | 0.0           | -               |
| 24.6988 | 2050 | 0.0           | -               |
| 25.3012 | 2100 | 0.0           | -               |
| 25.9036 | 2150 | 0.0           | -               |
| 26.5060 | 2200 | 0.0           | -               |
| 27.1084 | 2250 | 0.0           | -               |
| 27.7108 | 2300 | 0.0           | -               |
| 28.3133 | 2350 | 0.0           | -               |
| 28.9157 | 2400 | 0.0           | -               |
| 29.5181 | 2450 | 0.0           | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.3.1
- Transformers: 4.44.2
- PyTorch: 2.2.0a0+81ea7a4
- Datasets: 3.2.0
- Tokenizers: 0.19.1

## 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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