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---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 오픈페이스 스쿠터 오토바이 라이트 사계절 LED 남남녀공용 바이크 여성용 헬멧 스포츠/레저>오토바이/스쿠터>오토바이용품>헬멧
- text: 코미네 오토바이 핀 잠금 장치 도난 방지 디스크락 열쇠 스포츠/레저>오토바이/스쿠터>오토바이부품>잠금장치
- text: 방수 스즈끼 오토바이 방한복 배달 스즈키 우주복 겨울 오토바이 방한용품 스포츠/레저>오토바이/스쿠터>오토바이의류/잡화>상하세트
- text: 오토바이 스쿠터 리어백 트렁크 소형 탑박스 두꺼운 범용 스포츠/레저>오토바이/스쿠터>오토바이용품>기타오토바이용품
- text: 하템몰 오토바이 헬멧 블루투스 채터박스 BiT-3S 인터콤 스포츠/레저>오토바이/스쿠터>오토바이용품>기타오토바이용품
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 |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0.0 | <ul><li>'KR모터스 그란투스 125 스쿠터 스포츠/레저>오토바이/스쿠터>스쿠터>일반스쿠터'</li><li>'니키125 걸프 에디션 스쿠터 2024년 스포츠/레저>오토바이/스쿠터>스쿠터>일반스쿠터'</li><li>'AU테크 에코로 포니 전기스쿠터 12Ah 스포츠/레저>오토바이/스쿠터>스쿠터>전동스쿠터'</li></ul> |
| 3.0 | <ul><li>'오토바이 여름헬멧 오픈페이스 반모 경량 클래식 헬멧 빈티지 스틸 레트로 스포츠/레저>오토바이/스쿠터>오토바이용품>헬멧'</li><li>'레트로 바이크 헬멧 할리데이비슨 오토바이 감성 보 -레트로 미러 스포츠/레저>오토바이/스쿠터>오토바이용품>헬멧'</li><li>'남녀공용 편광 변색 선글라스 꽃가루 알레르기 방지 윈드 고글 UV400 보호 TR90 스포츠/레저>오토바이/스쿠터>오토바이용품>기타오토바이용품'</li></ul> |
| 4.0 | <ul><li>'코미네 3핑거 여름 반장갑 메쉬 글러브 BLACK-RED GK-260 스포츠/레저>오토바이/스쿠터>오토바이의류/잡화>바이크장갑'</li><li>'맥슬러 케블라 라이딩청바지 오토바이바지 M-2073 스포츠/레저>오토바이/스쿠터>오토바이의류/잡화>하의'</li><li>'오토바이 방한복 배달 남녀공용 일체형 방수 바이크 라이딩 배달우주복 스포츠/레저>오토바이/스쿠터>오토바이의류/잡화>상하세트'</li></ul> |
| 2.0 | <ul><li>'피렐리 엔젤 스쿠터 더뉴 PCX 뒤 타이어 120 스포츠/레저>오토바이/스쿠터>오토바이부품>기타오토바이부품'</li><li>'투스크 메가비트 레이디얼 타이어 28x1015 혼다 탈론 폭스 라이브 밸브용 1000X 스포츠/레저>오토바이/스쿠터>오토바이부품>기타오토바이부품'</li><li>'혼다 PCX125 브레이크 캘리퍼 핀 45215-KPH-951 스포츠/레저>오토바이/스쿠터>오토바이부품>기타오토바이부품'</li></ul> |
| 1.0 | <ul><li>'CFORCE 450 4륜 오토바이 스포츠/레저>오토바이/스쿠터>오토바이'</li><li>'ATV-A형 사륜오토바이 스포츠/레저>오토바이/스쿠터>오토바이'</li><li>'BMW F850GS 오토바이 스포츠/레저>오토바이/스쿠터>오토바이'</li></ul> |
| 5.0 | <ul><li>'자이로콥 G에스 전동투휠 4.4Ah 스포츠/레저>오토바이/스쿠터>전동휠'</li><li>'킹송 외발휠 16S 840Wh 스포츠/레저>오토바이/스쿠터>전동휠'</li><li>'나인봇 엘리트 플러스 전동휠 10Ah 스포츠/레저>오토바이/스쿠터>전동휠'</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_sl23")
# Run inference
preds = model("코미네 오토바이 핀 잠금 장치 도난 방지 디스크락 열쇠 스포츠/레저>오토바이/스쿠터>오토바이부품>잠금장치")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 3 | 8.3719 | 19 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0.0 | 70 |
| 1.0 | 70 |
| 2.0 | 70 |
| 3.0 | 70 |
| 4.0 | 70 |
| 5.0 | 13 |
### 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0141 | 1 | 0.4943 | - |
| 0.7042 | 50 | 0.4622 | - |
| 1.4085 | 100 | 0.1572 | - |
| 2.1127 | 150 | 0.0427 | - |
| 2.8169 | 200 | 0.0006 | - |
| 3.5211 | 250 | 0.0 | - |
| 4.2254 | 300 | 0.0 | - |
| 4.9296 | 350 | 0.0 | - |
| 5.6338 | 400 | 0.0 | - |
| 6.3380 | 450 | 0.0 | - |
| 7.0423 | 500 | 0.0 | - |
| 7.7465 | 550 | 0.0 | - |
| 8.4507 | 600 | 0.0 | - |
| 9.1549 | 650 | 0.0 | - |
| 9.8592 | 700 | 0.0 | - |
| 10.5634 | 750 | 0.0 | - |
| 11.2676 | 800 | 0.0 | - |
| 11.9718 | 850 | 0.0 | - |
| 12.6761 | 900 | 0.0001 | - |
| 13.3803 | 950 | 0.0 | - |
| 14.0845 | 1000 | 0.0 | - |
| 14.7887 | 1050 | 0.0 | - |
| 15.4930 | 1100 | 0.0 | - |
| 16.1972 | 1150 | 0.0 | - |
| 16.9014 | 1200 | 0.0 | - |
| 17.6056 | 1250 | 0.0 | - |
| 18.3099 | 1300 | 0.0 | - |
| 19.0141 | 1350 | 0.0 | - |
| 19.7183 | 1400 | 0.0 | - |
| 20.4225 | 1450 | 0.0 | - |
| 21.1268 | 1500 | 0.0 | - |
| 21.8310 | 1550 | 0.0 | - |
| 22.5352 | 1600 | 0.0 | - |
| 23.2394 | 1650 | 0.0 | - |
| 23.9437 | 1700 | 0.0 | - |
| 24.6479 | 1750 | 0.0 | - |
| 25.3521 | 1800 | 0.0 | - |
| 26.0563 | 1850 | 0.0 | - |
| 26.7606 | 1900 | 0.0 | - |
| 27.4648 | 1950 | 0.0 | - |
| 28.1690 | 2000 | 0.0 | - |
| 28.8732 | 2050 | 0.0 | - |
| 29.5775 | 2100 | 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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