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: 폭스밸리 프리미엄 자세교정밴드 말린 어깨 목 굽은등 라운드숄더 일자 바른 체형 교정기 M+L 폭스밸리
- text: 올그린 무릎 보조기 MCL 니케이지 인대 연골 보호대 수술후 의료용 니케이지_블루_XL 올그린
- text: 통풍형 목보호대 쿨링 경추 목디스크 목쿠션 거북목 여성용 hilala115
- text: THEPURE 목보호대 거북목 자세교정기 보조기 지지대 봄여름가을겨울 02. UIS-03_S 48CM 선셋
- text: 필라델피아 목보호대 SM-001 사이즈선택 경추보호대 릴렉스 목해먹 목스트레칭 목견인기 일자목 디아
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: 0.8887880986937591
      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:** 7 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                                                                                                                                                                                                                                           |
|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 6.0   | <ul><li>'반깁스 반기브스 다리보조기 골절 다리 수술 고정 오른발 ㅡ 기본 모델 올바른해외직구샵'</li><li>'아오스 의료용 무릎보호대 124 MCL [0002]S 왼쪽용 CJONSTYLE'</li><li>'원코어 발보조기 끌림방지 보호대 재활 장비 발목보조기 발지지대 왼발_L 키위프'</li></ul>                                                                    |
| 2.0   | <ul><li>'이화 밸포밴드 팔걸이 견지대 성인용 21061458 M 비앤비(best & BEST)'</li><li>'허리보조기 허리 척추 보호 의료용 AOS-460 여_XXL 링쿠'</li><li>'울트라슬링 어깨보호대 팔걸이 어깨수술 울트라실링 팔깁스 K 타입 디엘아이'</li></ul>                                                                               |
| 5.0   | <ul><li>'전동기립기 하반신 경사 스탠딩 편마비 침대 보조기 수동 높이 조절 + 사륜 + 식탁 쇼핑의품격001'</li><li>'물리치료기계 재활기 가정용 근육 도수 허리 승모근 단일 모델 에오인'</li><li>'환자 전동 침대 의료용 가정용 병원 전동기립기 보조 화이트 97cmx45cmx202cm 연림스토어'</li></ul>                                                       |
| 0.0   | <ul><li>'미제 재활 고무찰흙 퓨티 (살색/노랑/빨강/초록/파랑) 초록 텔레그라프'</li><li>'손가락 재활 장갑 편마비 손재활 운동 로봇 기구 주황색 미러링된 왼손 M 구구상회'</li><li>'건강누리 말렛핑거스프린트 리필(Mallet Finger Splint Refill) 오픈형7호 단위:팩(5개)  (주)엠디오씨'</li></ul>                                                |
| 4.0   | <ul><li>'통증바이 남녀공용 바른 자세밴드 3XL (허리둘레 ... 1개 XL (허리둘레 30~32인치) × 1개 이위에'</li><li>'굽은어깨 굽은등 어깨 허리 바른 자세 밴드 라운드숄더 펴주는 XXXL 아이엠어굿맨'</li><li>'(발음교정기 돌돌이) 스카이블루 학생용 영어 국어 발음연습 발음교정 하드(스카이블루) (주)애니덴'</li></ul>                                         |
| 1.0   | <ul><li>'[의료기기](반값딜) 넥가디언 거북목 디스크 교정기 쿠션형 견인기 단독 (밤색) 852헤르츠'</li><li>'바른 목 미라클 고급형 보호대 밴드  젬마줌마'</li><li>'[OFLP1Q84]허리E UP 통기성 에어메디칼 견인요 S 27인치이하／FREE sellerhub'</li></ul>                                                                       |
| 3.0   | <ul><li>'도고 렉스타 허벅지형 205 압박용밴드 의료용 압박스타킹 혈액순환 다리붓기 개선 의료기기 중압 (4)265발트임_살색_XL (주)도고메디칼'</li><li>'[GIN383R]종아리 압박밴드 스타킹 다리 간호사 수면 관리 블랙／FREE sellerhub'</li><li>'Duomed Advantage, 15-20 mmHg, 종아리 높이, 오픈 토 Small_Almond 수 스토리(SU STORY)'</li></ul> |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **all** | 0.8888 |

## 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_lh21")
# Run inference
preds = model("통풍형 목보호대 쿨링 경추 목디스크 목쿠션 거북목 여성용 hilala115")
```

<!--
### Downstream Use

*List how someone could finetune this model on their own dataset.*
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 9.96   | 21  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0.0   | 50                    |
| 1.0   | 50                    |
| 2.0   | 50                    |
| 3.0   | 50                    |
| 4.0   | 50                    |
| 5.0   | 50                    |
| 6.0   | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0182  | 1    | 0.4265        | -               |
| 0.9091  | 50   | 0.3097        | -               |
| 1.8182  | 100  | 0.0765        | -               |
| 2.7273  | 150  | 0.0638        | -               |
| 3.6364  | 200  | 0.0434        | -               |
| 4.5455  | 250  | 0.0035        | -               |
| 5.4545  | 300  | 0.0002        | -               |
| 6.3636  | 350  | 0.0001        | -               |
| 7.2727  | 400  | 0.0001        | -               |
| 8.1818  | 450  | 0.0001        | -               |
| 9.0909  | 500  | 0.0001        | -               |
| 10.0    | 550  | 0.0001        | -               |
| 10.9091 | 600  | 0.0001        | -               |
| 11.8182 | 650  | 0.0001        | -               |
| 12.7273 | 700  | 0.0001        | -               |
| 13.6364 | 750  | 0.0001        | -               |
| 14.5455 | 800  | 0.0001        | -               |
| 15.4545 | 850  | 0.0001        | -               |
| 16.3636 | 900  | 0.0           | -               |
| 17.2727 | 950  | 0.0           | -               |
| 18.1818 | 1000 | 0.0           | -               |
| 19.0909 | 1050 | 0.0001        | -               |
| 20.0    | 1100 | 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->