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---
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: 얌뚱이 칼라고무밴드 머리끈 헤어밴드 고무줄 유아 아동 여아 어린이집 검정 색 대용량 대핑크30g 얌뚱이
- text: 파티 벨벳 심플 왕리본핀 반묶음핀 30칼라 와인_납작핀대 릴리트리
- text: 넓은 여자 머리띠 윤아 와이드 귀안아픈 면 니트 터번 T-도톰쫀득_핑크 모스블랑
- text: 얼굴소멸 히메컷 가발 앞머리 사이드뱅 옆머리 부분 가발 애교머리 풀뱅 규리 민니 옆2p-라이트브라운 굿모닝리테일
- text: 13cm 빅사이즈 대왕 숱많은 긴 머리 꼬임 올림머리 집게핀 3/ 그라데이션 매트_브라운 블렌디드
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: 0.9541466176054345
      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:** 5 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>'(신세계김해점)에트로 프로푸미 헤어밴드 01046 05 1099 ONE SIZE 신세계백화점'</li><li>'Baby scrunchie 3set (White/Beige/Black) 빌라드실크 곱창밴드 미니 실크 스크런치 세트  주식회사 실크랩'</li><li>'간단 헤어밴드 미키마우스 머리띠 왕 리본 남자 캐릭터 플라스틱 반짝이 1-4. 글리터 / 블랙 아이드림'</li></ul> |
| 1.0   | <ul><li>'위즈템 헤어밴드 진주 크리스탈 머리끈 연핑크 파파닐'</li><li>'둥근고무줄 (대용량) 칼라 금 은 천고무줄 벌크 탄성끈 가는줄 /굵은줄 02. 대용량 굵은줄(2.5mmx60M)_금색 마이1004(MY1004)'</li><li>'천연 컬러 고무 끈 고무줄 생활용품 3M 하늘색 제이앤제이웍스'</li></ul>                                         |
| 0.0   | <ul><li>'인모 남자가발 정수리 커버 자연스러운 O형 커버가발 마오_인모14X14 하이윤'</li><li>'얼굴소멸 히메컷 가발 앞머리 사이드뱅 옆머리 부분 히메컷 사이드뱅 옆2p-내츄럴브라운 와우마켓'</li><li>'얼굴소멸 히메컷 가발 앞머리 사이드뱅 옆머리 부분 옆2p-라이트브라운 이지구'</li></ul>                                              |
| 4.0   | <ul><li>'무지 12컬러 심플 리본 바나나핀 핫핑크 하얀당나귀'</li><li>'네임핀/이름핀/네임브로치/어린이집선물/유치원선물 5글자(영어6자~8자)_별_브로치 쭈스타'</li><li>'메탈 셀룰로오스 꼬임 올림머리 집게핀 사각4170_아이스옐로우 엑스엔서'</li></ul>                                                                   |
| 3.0   | <ul><li>'웨딩 드레스 유니크 베일 셀프 촬영 소품 대형 리본 잡지 모델 패션쇼 장식 액세서리 머리 04.파란 (핸드메이드) 더비공이(TheB02)'</li><li>'슈퍼 요정 흰색 보석 웨딩 헤어 타워 공연 여행 T15-a_선택하세요 아토버디'</li><li>'뿌리볼륨집게3p  건강드림'</li></ul>                                                  |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.9541 |

## 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_ac16")
# Run inference
preds = model("파티 벨벳 심플 왕리본핀 반묶음핀 30칼라 와인_납작핀대 릴리트리")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 9.956  | 24  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 50                    |
| 1.0   | 50                    |
| 2.0   | 50                    |
| 3.0   | 50                    |
| 4.0   | 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 |
|:-----:|:----:|:-------------:|:---------------:|
| 0.025 | 1    | 0.4499        | -               |
| 1.25  | 50   | 0.2065        | -               |
| 2.5   | 100  | 0.0446        | -               |
| 3.75  | 150  | 0.0001        | -               |
| 5.0   | 200  | 0.0           | -               |
| 6.25  | 250  | 0.0001        | -               |
| 7.5   | 300  | 0.0           | -               |
| 8.75  | 350  | 0.0           | -               |
| 10.0  | 400  | 0.0           | -               |
| 11.25 | 450  | 0.0           | -               |
| 12.5  | 500  | 0.0           | -               |
| 13.75 | 550  | 0.0           | -               |
| 15.0  | 600  | 0.0           | -               |
| 16.25 | 650  | 0.0           | -               |
| 17.5  | 700  | 0.0           | -               |
| 18.75 | 750  | 0.0           | -               |
| 20.0  | 800  | 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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