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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: 아이워너 스마트체지방체중계KS-BF4000/홈트용품/헬스용품 더베스트샾
- text: 트랜스텍 팔뚝형 가정용 자동 혈압계 혈압측정기 TMB-1597 상승가압방식 바이메드
- text: 휴비딕 초음파 무선 신장계 HUK-2 아기 키측정기 키재기 자동 거리 G 핑크 골든 플레이스
- text: 앳플리 T9 정확한 몸무게 저울 더블스마트인 체중계 가정용 전자 기계 화이트 (주)픽스몰
- text: 어린이 신장 측정기 높이 벽 스티커 3D 키재기 신장계 B_큰 핑팝
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: 0.971224790949336
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 |
|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1.0 | <ul><li>'신장 측정 기계 표준 기계식 측정기 학교 병원 약국 건강 검진 신장계 키재기 70-190cm 블랙_기계적 핑팝'</li><li>'공룡 자석 귀여운 키재기 기린 눈금 벽 측정도구 소프트웨어 보내기[프로 버전-양고] 고소몽 새벽잡화점'</li><li>'키 몸무게 측정기 검사 헬스장 학교 검진 보건실 신체 160kg 제품 (블랙) 노마둔'</li></ul> |
| 5.0 | <ul><li>'휴비딕 신생아 유아 아기 고양이 강아지 반려동물 체중계 HUS-316B (주)휴비딕'</li><li>'[애구애구] 강아지 고양이 체중계 건전지 포함, 애견 원터치 무선 체중계, 반려견 몸무계 측정기 애드마스터'</li><li>'상업용 전자 정밀 소형 저울T사우나 헬스장 체중계 전자저울 100KG 150KG 이로운발견'</li></ul> |
| 0.0 | <ul><li>'스마트 만보기 시계 만보팔찌 손목만보계 칼로리시계 스마트 만보 시계 팔찌 손목 형 실리콘 디지털 계 만보기시계-민트 제이한 주식회사'</li><li>'오리온 계수기 FH102 주식회사 다원피앤피'</li><li>'미니 디지털카운터기 0~99999까지 / 반지계수기 카운터기-파랑 대박나라'</li></ul> |
| 4.0 | <ul><li>'브라운 써모스캔 귀 체온계 IRT6030 롯데백화점1관'</li><li>'브라운체온계 IRT-6030 적외선 귀체온계 가정용 신생아 체온계 필터21개+건전지 포함 브라운체온계 IRT-6030 주식회사 온라이브플러스'</li><li>'브라운 귀체온계 IRT-6030 + 필터21p포함/1년무상AS baby 신세계몰'</li></ul> |
| 6.0 | <ul><li>'오므론 손목형 자동전자 혈압계 HEM-6161 가정용혈압계_MC 멸치쇼핑'</li><li>'인바디 BPBIO320N 자동 혈압계 BPBIO320N_그레이(테이블+의자 포함) 바디메디칼'</li><li>'휴비딕 비피첵 손목 자동 전자 혈압계 HBP-600 혈압측정기 판테온'</li></ul> |
| 3.0 | <ul><li>'독일 LED 검이경(성인/아동 겸용)-건전지식 풍솔글로벌'</li><li>'간호사용 병원 진찰용품 의사 청진기 측정기 소아과 심박 내과 cosse2'</li><li>'SPIRIT 검이경 CK-939A /오토스코프/직접조사방식/알루미늄재질/좌우스위치방식적용 풍솔글로벌'</li></ul> |
| 2.0 | <ul><li>'Wahoo Fitness 티커 심박수측정기(HRM) 스텔스 그레이 White 픽더마인드'</li><li>'POLAR Equine H10 라이딩 심박수 센서 라이브러리2'</li><li>'Polar H10 심박수 모니터, 블루투스 HRM 가슴 스트랩 - 아이폰 및 안드로이드 호환, 블랙 식스퀄리티'</li></ul> |
## Evaluation
### Metrics
| Label | Metric |
|:--------|:-------|
| **all** | 0.9712 |
## 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_lh1")
# Run inference
preds = model("어린이 신장 측정기 높이 벽 스티커 3D 키재기 신장계 B_큰 핑팝")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 3 | 9.9771 | 18 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0.0 | 50 |
| 1.0 | 50 |
| 2.0 | 6 |
| 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0208 | 1 | 0.4314 | - |
| 1.0417 | 50 | 0.1947 | - |
| 2.0833 | 100 | 0.0912 | - |
| 3.125 | 150 | 0.0968 | - |
| 4.1667 | 200 | 0.0231 | - |
| 5.2083 | 250 | 0.0004 | - |
| 6.25 | 300 | 0.0001 | - |
| 7.2917 | 350 | 0.0001 | - |
| 8.3333 | 400 | 0.0 | - |
| 9.375 | 450 | 0.0001 | - |
| 10.4167 | 500 | 0.0 | - |
| 11.4583 | 550 | 0.0 | - |
| 12.5 | 600 | 0.0 | - |
| 13.5417 | 650 | 0.0 | - |
| 14.5833 | 700 | 0.0 | - |
| 15.625 | 750 | 0.0 | - |
| 16.6667 | 800 | 0.0 | - |
| 17.7083 | 850 | 0.0 | - |
| 18.75 | 900 | 0.0 | - |
| 19.7917 | 950 | 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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