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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: 코어슬리머 전용 리필패드 6P 2개 롯데아이몰
- text: 발락 손목 마사지기 안마기 간편한 EMS 반영구적 통증 팔목 마사지 발락 손목 마사지기 세트 (주)엘가니
- text: '[바이오프로테크]프로텐스 핀타입 대형 저주파패드 2조(RG01) '
- text: 성게 탱탱볼 노인복지센터 안마볼 촉각볼 선물 몸신 물리치료 어르신 탱볼_11.탱볼(농구) 워커스
- text: '[약손드림] 저주파 EMS 어깨 마사지기 미세전류 휴대용 안마기 부모님선물 효도선물 어깨보호대 M(95~100호) 금양리테일 주식회사'
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: 0.894511760513186
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>'예림전자 적외선조사기 전체화이트 필립스 250W 램프 적외선 치료기 아닌 국산 의료기기 01 전체화이트 e청춘'</li><li>'비타그램 필립스 적외선 램프 피부방사기 WGT-8888S VitaGRAM'</li><li>'원적외선 온열 치료기 한의원 어깨 경추 램프 마사지 MinSellAmount 차류소'</li></ul> |
| 2.0 | <ul><li>'HWATO 고급형 부항기 14컵 라이프샵'</li><li>'손 사혈부항용 따주기 자 통사혈기 광명사 침 구비 측정 습식 손따주는 체했을때 혈당기 자동 간편 알리몽드투'</li><li>'한솔부항기 신형 소독가능 부항컵 10개 1박스 (사이즈선택1-5호) 한솔부항2호컵 수의료기'</li></ul> |
| 5.0 | <ul><li>'오므론 저주파 롱 라이프 패드 2p HV-LLPAD-G... 1개 HV-LLPAD-GY × 2개 스위에'</li><li>'코어슬리머 전용 리필패드 6P 3개 [0001]기본상품 CJONSTYLE'</li><li>'클럭 미니 마사지기 리필패드 큰패드 2박스 총6P /DY_MC 멸치쇼핑'</li></ul> |
| 0.0 | <ul><li>'닥터체크 슬림 X형 테이핑 무릎보호대(좌우겸용 1P) M-중형(630475) 트래이드 씨스템(TRADE SYSTEM)'</li><li>'닥터체크 슬림 X형 테이핑 종아리압박보호대(좌우겸용 1P) M-중형(630499) 태빛ID'</li><li>'국산 의료용 허리보호대 편안하고 부드러운 허리복대 선택01- 001s 허리보호대_XXXL(40~43인치) 대한건강'</li></ul> |
| 4.0 | <ul><li>'스트라텍 의료용 전침기 4채널 STN-220 저주파자극기 침전기자극기 자석형 (주)오픈메디칼'</li><li>'디웰 저주파 마사지기 버튼형 LB-1803 미니마사지기 휴대용 무선 안마기 일반구매_06.버튼형2박스+대형패드 8매+흡착컵8개 주식회사 청훈'</li><li>'극동저주파 PRO1000 wave GOLD 헬스푸드메디칼'</li></ul> |
| 1.0 | <ul><li>'조은팜 초음파젤 의료용젤 투명5L 1통 무료전달 조은초음파젤5L블루 세븐메디컬'</li><li>'이도팜 소노젤리 투명 블루 5L +250ml 공병 소노겔 초음파젤리 ECG [0001]블루 5L CJONSTYLE'</li><li>'세니피아 에코소닉 초음파젤 투명 250mL 12개x4통 1박스 소노젤리 피부과 산부인과용 세븐메디컬'</li></ul> |
| 3.0 | <ul><li>'클럭 미니 마사지기SE YGGlobal'</li><li>'온열/공기압/원적외선/저주파 4중케어 무릎마사지기[공기압 온열 원적외선 진동기능]안마기 05.클레버 마사지건 SR825 수련닷컴'</li><li>'휴테크 하체 근육 강화 EMS 마사지기 식스패드 풋핏2 HT-W03A '</li></ul> |
## Evaluation
### Metrics
| Label | Metric |
|:--------|:-------|
| **all** | 0.8945 |
## 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_lh9")
# Run inference
preds = model("코어슬리머 전용 리필패드 6P 2개 롯데아이몰")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 3 | 9.78 | 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 | 0.4065 | - |
| 0.9091 | 50 | 0.2829 | - |
| 1.8182 | 100 | 0.0954 | - |
| 2.7273 | 150 | 0.0196 | - |
| 3.6364 | 200 | 0.0057 | - |
| 4.5455 | 250 | 0.0069 | - |
| 5.4545 | 300 | 0.0024 | - |
| 6.3636 | 350 | 0.0003 | - |
| 7.2727 | 400 | 0.0002 | - |
| 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 | 0.0001 | - |
| 17.2727 | 950 | 0.0001 | - |
| 18.1818 | 1000 | 0.0001 | - |
| 19.0909 | 1050 | 0.0 | - |
| 20.0 | 1100 | 0.0001 | - |
### 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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