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---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 서핑 보드 패들 바디 스킴 웨이크 서핑 여름휴가 물놀이 스포츠/레저>수영>수영용품>기타수영용품
- text: 체형커버 수영복 워터파크 온천 호캉스 비치웨어 심플 케주얼 스포츠 스커트형 수영복 스포츠/레저>수영>비치웨어>스커트
- text: 헤링본 여성 래쉬가드팬츠 스판 보드숏 수영복 반바지 비치웨어 휴양지 스윔웨어 AD508W 스포츠/레저>수영>비치웨어>팬츠
- text: 레노마수영복 여성 파레오랩스커트 WS20307 스포츠/레저>수영>비치웨어>스커트
- text: 래쉬가드 스노클링 남성 전신 긴팔 방한 바다 수영복세트 스포츠/레저>수영>남성수영복>전신수영복
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:** 4 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>'배럴 맨 에센셜 스탠다드핏 집업 래쉬가드 B4SMWZR101BLK 스포츠/레저>수영>비치웨어>상의'</li><li>'여성 래쉬가드 집업 비치 웨어 커플 수영복 스포츠/레저>수영>비치웨어>커플비치웨어'</li><li>'엘르 엘르스포츠 남성 트렁크 비치 NVY E3SMOMJ01 스포츠/레저>수영>비치웨어>팬츠'</li></ul> |
| 2.0 | <ul><li>'아레나 오리발 롱핀 WHT265 A3AC1AF01WHT265 스포츠/레저>수영>수영용품>오리발'</li><li>'패들보드 서핑 공기주입식 웨이크 보드 스탠드 풀세트 스포츠/레저>수영>수영용품>기타수영용품'</li><li>'아레나 아레나 킥보드 A3AC1AK01YEL 스포츠/레저>수영>수영용품>기타수영용품'</li></ul> |
| 0.0 | <ul><li>'스피도 남성 스탠다드 수영복 사각 다리 스플라이스 로고 피코트 스몰 스포츠/레저>수영>남성수영복>반신수영복'</li><li>'아레나 와트 아동레저 슈트 A3BB1BI23NVY-MN 스포츠/레저>수영>남성수영복>반신수영복'</li><li>'남자 수영복 전신 슈트 스포츠/레저>수영>남성수영복>전신수영복'</li></ul> |
| 3.0 | <ul><li>'아레나 여성 비키니 2PCS 수영복 A0BL1PS09BLK 스포츠/레저>수영>여성수영복>비키니'</li><li>'실내수영장 체형커버 수영복 풀빌라 온천 빅 사이즈 스포츠/레저>수영>여성수영복>원피스수영복'</li><li>'빅 사이즈 투피스 비키니 심플 올오버 프린트 수영복 624538 스포츠/레저>수영>여성수영복>비키니'</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_sl16")
# Run inference
preds = model("레노마수영복 여성 파레오랩스커트 WS20307 스포츠/레저>수영>비치웨어>스커트")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 3 | 8.4071 | 21 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0.0 | 70 |
| 1.0 | 70 |
| 2.0 | 70 |
| 3.0 | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0182 | 1 | 0.4884 | - |
| 0.9091 | 50 | 0.4351 | - |
| 1.8182 | 100 | 0.1675 | - |
| 2.7273 | 150 | 0.0769 | - |
| 3.6364 | 200 | 0.0023 | - |
| 4.5455 | 250 | 0.0001 | - |
| 5.4545 | 300 | 0.0 | - |
| 6.3636 | 350 | 0.0 | - |
| 7.2727 | 400 | 0.0 | - |
| 8.1818 | 450 | 0.0 | - |
| 9.0909 | 500 | 0.0 | - |
| 10.0 | 550 | 0.0 | - |
| 10.9091 | 600 | 0.0 | - |
| 11.8182 | 650 | 0.0 | - |
| 12.7273 | 700 | 0.0 | - |
| 13.6364 | 750 | 0.0 | - |
| 14.5455 | 800 | 0.0 | - |
| 15.4545 | 850 | 0.0 | - |
| 16.3636 | 900 | 0.0 | - |
| 17.2727 | 950 | 0.0 | - |
| 18.1818 | 1000 | 0.0 | - |
| 19.0909 | 1050 | 0.0 | - |
| 20.0 | 1100 | 0.0 | - |
| 20.9091 | 1150 | 0.0 | - |
| 21.8182 | 1200 | 0.0 | - |
| 22.7273 | 1250 | 0.0 | - |
| 23.6364 | 1300 | 0.0 | - |
| 24.5455 | 1350 | 0.0 | - |
| 25.4545 | 1400 | 0.0 | - |
| 26.3636 | 1450 | 0.0 | - |
| 27.2727 | 1500 | 0.0 | - |
| 28.1818 | 1550 | 0.0 | - |
| 29.0909 | 1600 | 0.0 | - |
| 30.0 | 1650 | 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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