---
base_model: klue/roberta-base
library_name: setfit
metrics:
- metric
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 뉴발란스패딩 BQC NBNPB41043-16 UNI 액티브 숏 나일론 구스다운 자켓 105 (주)씨제이이엔엠
- text: 드로우핏X노이어 핸드메이드 캐시미어 싱글 코트 DRAW FIT X NOIRER HANDMADE CASHMERE SINGLE COAT
550182 M 버베나
- text: 언더아머 야구 점퍼 1375292-400 S 슈즈스타11
- text: '[Lucky Brand] 럭키브랜드 23FW 슬림핏 코듀로이 팬츠 1종 크림_55 (주)씨제이이엔엠'
- text: '[롯데백화점]탱커스 바스락 후드 여름 점퍼 (TV1JP013M0) 블랙_F 롯데백화점_'
inference: true
model-index:
- name: SetFit with klue/roberta-base
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: metric
value: 0.8999370266909948
name: Metric
---
# SetFit with klue/roberta-base
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [klue/roberta-base](https://huggingface.co/klue/roberta-base) 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:** [klue/roberta-base](https://huggingface.co/klue/roberta-base)
- **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
### 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 | - '갤러리아 GUESS Jeans S/S [공용] NO1D0023 M톤 슬림 와이드 미디엄블루_28 갤러리아백화점'
- '[현대백화점][헤지스남성] 케이블 울 하프 집업 니트 HZSW3D326G2 [00004] 그레이(G2)/110 (주)현대홈쇼핑'
- '데일리 플랩 항공 점퍼BK BK_110 (주) 패션플러스'
|
| 2.0 | - '스파오 산리오캐릭터즈 수면잠옷BLACKSPPPD4TU03 SPPPD4TU03 19 BLACK_L 100 시그마인터내셔널'
- 'BYC여성 순면내복내의 베이직여상하2호 BYT6656 베이직여상하_인디안핑크_90 세종유통'
- 'BYT3842 BYC 데오니아 심플 순면 여자 끈 나시 런닝 검정색_100 에이치앤비 주식회사'
|
| 3.0 | - '[켄지 24SS 최신상] ○ 24SS 오가닉 코튼 100 니트 4종 105 '
- '[갤러리아] 울 아가일 배색 가디건(한화갤러리아㈜ 센터시티) 라이트그레이LG82020_66 한화갤러리아(주)'
- '[오우오](신세계의정부점)벨리SET / W3F91ST03 핑크_FR 주식회사 에스에스지닷컴'
|
| 0.0 | - '[현대백화점]엘르이너웨어_ EBMRN713BK 모달에어로웜와플 남런닝BK 95 (주)현대백화점'
- '비너스(정상) 비너스 면 80수 이합 지그재그 나염 남성 런닝 트렁크 세트_A VMV41 블루(BU)/100_필수선택 (주) 패션플러스'
- 'JHMRU007 제임스딘 순면 V넥 남성 민소매 머슬 런닝 2_110 도도shop'
|
## Evaluation
### Metrics
| Label | Metric |
|:--------|:-------|
| **all** | 0.8999 |
## 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_item_ap")
# Run inference
preds = model("언더아머 야구 점퍼 1375292-400 S 슈즈스타11")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 3 | 9.6403 | 24 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0.0 | 300 |
| 1.0 | 809 |
| 2.0 | 457 |
| 3.0 | 1050 |
### 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0024 | 1 | 0.4029 | - |
| 0.1222 | 50 | 0.3584 | - |
| 0.2445 | 100 | 0.2822 | - |
| 0.3667 | 150 | 0.2453 | - |
| 0.4890 | 200 | 0.1961 | - |
| 0.6112 | 250 | 0.1677 | - |
| 0.7335 | 300 | 0.1175 | - |
| 0.8557 | 350 | 0.0615 | - |
| 0.9780 | 400 | 0.0308 | - |
| 1.1002 | 450 | 0.0218 | - |
| 1.2225 | 500 | 0.0133 | - |
| 1.3447 | 550 | 0.0058 | - |
| 1.4670 | 600 | 0.0101 | - |
| 1.5892 | 650 | 0.002 | - |
| 1.7115 | 700 | 0.0022 | - |
| 1.8337 | 750 | 0.0023 | - |
| 1.9560 | 800 | 0.0041 | - |
| 2.0782 | 850 | 0.0057 | - |
| 2.2005 | 900 | 0.0001 | - |
| 2.3227 | 950 | 0.0029 | - |
| 2.4450 | 1000 | 0.0032 | - |
| 2.5672 | 1050 | 0.004 | - |
| 2.6895 | 1100 | 0.0021 | - |
| 2.8117 | 1150 | 0.0033 | - |
| 2.9340 | 1200 | 0.002 | - |
| 3.0562 | 1250 | 0.002 | - |
| 3.1785 | 1300 | 0.0019 | - |
| 3.3007 | 1350 | 0.0 | - |
| 3.4230 | 1400 | 0.0019 | - |
| 3.5452 | 1450 | 0.0 | - |
| 3.6675 | 1500 | 0.0039 | - |
| 3.7897 | 1550 | 0.0 | - |
| 3.9120 | 1600 | 0.0 | - |
| 4.0342 | 1650 | 0.0002 | - |
| 4.1565 | 1700 | 0.0049 | - |
| 4.2787 | 1750 | 0.002 | - |
| 4.4010 | 1800 | 0.0 | - |
| 4.5232 | 1850 | 0.0026 | - |
| 4.6455 | 1900 | 0.0 | - |
| 4.7677 | 1950 | 0.0 | - |
| 4.8900 | 2000 | 0.0001 | - |
| 5.0122 | 2050 | 0.002 | - |
| 5.1345 | 2100 | 0.002 | - |
| 5.2567 | 2150 | 0.0 | - |
| 5.3790 | 2200 | 0.0 | - |
| 5.5012 | 2250 | 0.0 | - |
| 5.6235 | 2300 | 0.0 | - |
| 5.7457 | 2350 | 0.0004 | - |
| 5.8680 | 2400 | 0.0019 | - |
| 5.9902 | 2450 | 0.0018 | - |
| 6.1125 | 2500 | 0.0 | - |
| 6.2347 | 2550 | 0.0 | - |
| 6.3570 | 2600 | 0.0 | - |
| 6.4792 | 2650 | 0.0 | - |
| 6.6015 | 2700 | 0.002 | - |
| 6.7237 | 2750 | 0.0009 | - |
| 6.8460 | 2800 | 0.0 | - |
| 6.9682 | 2850 | 0.0015 | - |
| 7.0905 | 2900 | 0.0001 | - |
| 7.2127 | 2950 | 0.0001 | - |
| 7.3350 | 3000 | 0.002 | - |
| 7.4572 | 3050 | 0.0001 | - |
| 7.5795 | 3100 | 0.0001 | - |
| 7.7017 | 3150 | 0.0019 | - |
| 7.8240 | 3200 | 0.0019 | - |
| 7.9462 | 3250 | 0.0 | - |
| 8.0685 | 3300 | 0.0001 | - |
| 8.1907 | 3350 | 0.0038 | - |
| 8.3130 | 3400 | 0.0 | - |
| 8.4352 | 3450 | 0.0018 | - |
| 8.5575 | 3500 | 0.0 | - |
| 8.6797 | 3550 | 0.0019 | - |
| 8.8020 | 3600 | 0.0 | - |
| 8.9242 | 3650 | 0.0 | - |
| 9.0465 | 3700 | 0.0 | - |
| 9.1687 | 3750 | 0.0 | - |
| 9.2910 | 3800 | 0.0 | - |
| 9.4132 | 3850 | 0.0001 | - |
| 9.5355 | 3900 | 0.0 | - |
| 9.6577 | 3950 | 0.0019 | - |
| 9.7800 | 4000 | 0.0019 | - |
| 9.9022 | 4050 | 0.0 | - |
| 10.0244 | 4100 | 0.0001 | - |
| 10.1467 | 4150 | 0.0 | - |
| 10.2689 | 4200 | 0.002 | - |
| 10.3912 | 4250 | 0.0 | - |
| 10.5134 | 4300 | 0.0 | - |
| 10.6357 | 4350 | 0.0 | - |
| 10.7579 | 4400 | 0.0 | - |
| 10.8802 | 4450 | 0.0 | - |
| 11.0024 | 4500 | 0.0 | - |
| 11.1247 | 4550 | 0.0018 | - |
| 11.2469 | 4600 | 0.0 | - |
| 11.3692 | 4650 | 0.0 | - |
| 11.4914 | 4700 | 0.0 | - |
| 11.6137 | 4750 | 0.0 | - |
| 11.7359 | 4800 | 0.0019 | - |
| 11.8582 | 4850 | 0.001 | - |
| 11.9804 | 4900 | 0.0 | - |
| 12.1027 | 4950 | 0.0001 | - |
| 12.2249 | 5000 | 0.0 | - |
| 12.3472 | 5050 | 0.0 | - |
| 12.4694 | 5100 | 0.0 | - |
| 12.5917 | 5150 | 0.0 | - |
| 12.7139 | 5200 | 0.0 | - |
| 12.8362 | 5250 | 0.0 | - |
| 12.9584 | 5300 | 0.0 | - |
| 13.0807 | 5350 | 0.0001 | - |
| 13.2029 | 5400 | 0.0001 | - |
| 13.3252 | 5450 | 0.0 | - |
| 13.4474 | 5500 | 0.0001 | - |
| 13.5697 | 5550 | 0.0 | - |
| 13.6919 | 5600 | 0.0 | - |
| 13.8142 | 5650 | 0.0 | - |
| 13.9364 | 5700 | 0.0 | - |
| 14.0587 | 5750 | 0.0001 | - |
| 14.1809 | 5800 | 0.0 | - |
| 14.3032 | 5850 | 0.0 | - |
| 14.4254 | 5900 | 0.0 | - |
| 14.5477 | 5950 | 0.0 | - |
| 14.6699 | 6000 | 0.0 | - |
| 14.7922 | 6050 | 0.0 | - |
| 14.9144 | 6100 | 0.0 | - |
| 15.0367 | 6150 | 0.0 | - |
| 15.1589 | 6200 | 0.0 | - |
| 15.2812 | 6250 | 0.0 | - |
| 15.4034 | 6300 | 0.0 | - |
| 15.5257 | 6350 | 0.0 | - |
| 15.6479 | 6400 | 0.0 | - |
| 15.7702 | 6450 | 0.0 | - |
| 15.8924 | 6500 | 0.0 | - |
| 16.0147 | 6550 | 0.0 | - |
| 16.1369 | 6600 | 0.0 | - |
| 16.2592 | 6650 | 0.0 | - |
| 16.3814 | 6700 | 0.0 | - |
| 16.5037 | 6750 | 0.0 | - |
| 16.6259 | 6800 | 0.0 | - |
| 16.7482 | 6850 | 0.0 | - |
| 16.8704 | 6900 | 0.0 | - |
| 16.9927 | 6950 | 0.0 | - |
| 17.1149 | 7000 | 0.0 | - |
| 17.2372 | 7050 | 0.0 | - |
| 17.3594 | 7100 | 0.0 | - |
| 17.4817 | 7150 | 0.0 | - |
| 17.6039 | 7200 | 0.0 | - |
| 17.7262 | 7250 | 0.0 | - |
| 17.8484 | 7300 | 0.0 | - |
| 17.9707 | 7350 | 0.0 | - |
| 18.0929 | 7400 | 0.0 | - |
| 18.2152 | 7450 | 0.0 | - |
| 18.3374 | 7500 | 0.0 | - |
| 18.4597 | 7550 | 0.0 | - |
| 18.5819 | 7600 | 0.0 | - |
| 18.7042 | 7650 | 0.0 | - |
| 18.8264 | 7700 | 0.0 | - |
| 18.9487 | 7750 | 0.0 | - |
| 19.0709 | 7800 | 0.0 | - |
| 19.1932 | 7850 | 0.0 | - |
| 19.3154 | 7900 | 0.0 | - |
| 19.4377 | 7950 | 0.0 | - |
| 19.5599 | 8000 | 0.0 | - |
| 19.6822 | 8050 | 0.0 | - |
| 19.8044 | 8100 | 0.0 | - |
| 19.9267 | 8150 | 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}
}
```