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---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 피클볼라켓 가족용 나무 패들 초보자 라켓 메쉬 캐리 백 스포츠/레저>수련용품>기타수련용품
- text: 미즈노 복싱화 레슬링화 권투화 피니셔 미드 FINISHER MID 스포츠/레저>수련용품>수련화
- text: 프랭클린 스포츠 사이즈 콘홀 백 - 8 프리미엄 6 헤비 듀티 더블 스티치 캔버스 스포츠/레저>수련용품>기타수련용품
- text: 미즈노 복싱화 권투화 이지 스펙트라 37 플래시 그린 X 05 테두리 BM518 스포츠/레저>수련용품>수련화
- text: 주짓수 경량 도복 상하세트 훈련 남성 여성 통기성 스포츠/레저>수련용품>무도복
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:** 5 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
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### 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 |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 4.0 | <ul><li>'레슬링화 신발 남성 권투화 전문 훈련 복싱용품 복싱 남녀공용 트레이닝 스포츠/레저>수련용품>수련화'</li><li>'아디다스 복싱 스피덱스18 복싱화 FZ5308 스포츠/레저>수련용품>수련화'</li><li>'여성 복싱화 킥복싱 신발 권투화 운동화-514 스포츠/레저>수련용품>수련화'</li></ul> |
| 0.0 | <ul><li>'HK 조립식송판 태권도 격파판 격투기 용품 스포츠/레저 > 수련용품 > 격파용품'</li><li>'격파 용품 나무 격파판 나무송판 행사용 태권도 격파용 9mm 송판 50장묶음 스포츠/레저 > 수련용품 > 격파용품'</li><li>'무토 중급자용 플라스틱 송판 62kg 스포츠/레저>수련용품>격파용품'</li></ul> |
| 3.0 | <ul><li>'케이네트워크 컨텐더 시합용 주짓수도복 펄위브 도복 CJW-554WR 스포츠/레저>수련용품>무도복'</li><li>'주짓수 도복 기모노 훈련복 어린이 성인 여성 스포츠/레저>수련용품>무도복'</li><li>'무에타이 트렁크 쇼츠 바지 격투기 UFC 권투 팬츠 파이트 MMA 킥복싱 반바지 스포츠/레저>수련용품>무도복'</li></ul> |
| 1.0 | <ul><li>'전동 포일보드 방수 고출력 이포일 하이드로 윈드 스포츠/레저>수련용품>기타수련용품'</li><li>'남성과 여성을위한 전문 승마 초박형 속건 바지 흰색 경쟁 훈련 장비 실리콘 스포츠/레저>수련용품>기타수련용품'</li><li>'Weaver 가죽 벨트 블랭크 스냅 구멍 스포츠/레저>수련용품>기타수련용품'</li></ul> |
| 2.0 | <ul><li>'다오코리아 유도 태권도 주짓수 검정띠 자수포함 품띠 검은띠 유단자띠 스포츠/레저 > 수련용품 > 띠/벨트'</li><li>'아디다스 벨트 태권도 유급자 색 띠 스포츠/레저 > 수련용품 > 띠/벨트'</li><li>'아디다스 유도벨트 띠 선수용띠 국가대표 실업팀 대회띠 유도선수용 블랙밸트 스포츠/레저 > 수련용품 > 띠/벨트'</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_sl15")
# Run inference
preds = model("주짓수 경량 도복 상하세트 훈련 남성 여성 통기성 스포츠/레저>수련용품>무도복")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 3 | 9.7851 | 20 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0.0 | 9 |
| 1.0 | 70 |
| 2.0 | 9 |
| 3.0 | 70 |
| 4.0 | 70 |
### 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0222 | 1 | 0.4899 | - |
| 1.1111 | 50 | 0.4031 | - |
| 2.2222 | 100 | 0.0374 | - |
| 3.3333 | 150 | 0.0 | - |
| 4.4444 | 200 | 0.0 | - |
| 5.5556 | 250 | 0.0 | - |
| 6.6667 | 300 | 0.0 | - |
| 7.7778 | 350 | 0.0 | - |
| 8.8889 | 400 | 0.0 | - |
| 10.0 | 450 | 0.0 | - |
| 11.1111 | 500 | 0.0 | - |
| 12.2222 | 550 | 0.0 | - |
| 13.3333 | 600 | 0.0 | - |
| 14.4444 | 650 | 0.0 | - |
| 15.5556 | 700 | 0.0 | - |
| 16.6667 | 750 | 0.0 | - |
| 17.7778 | 800 | 0.0 | - |
| 18.8889 | 850 | 0.0 | - |
| 20.0 | 900 | 0.0 | - |
| 21.1111 | 950 | 0.0 | - |
| 22.2222 | 1000 | 0.0 | - |
| 23.3333 | 1050 | 0.0 | - |
| 24.4444 | 1100 | 0.0 | - |
| 25.5556 | 1150 | 0.0 | - |
| 26.6667 | 1200 | 0.0 | - |
| 27.7778 | 1250 | 0.0 | - |
| 28.8889 | 1300 | 0.0 | - |
| 30.0 | 1350 | 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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