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---
base_model: klue/roberta-base
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 네일스케치 2IN1 접착 홀로그램 필름 네일스티커 스톤 × 1개 LotteOn > 뷰티 > 네일 > 네일케어소품 LotteOn > 뷰티
> 네일 > 네일케어소품
- text: 오피아이 프로스파 네일 큐티클 오일 14.8ml × 1개 (#M)쿠팡 홈>뷰티>네일>큐티클/영양>큐티클케어 Coupang > 뷰티 >
네일 > 큐티클/영양 > 큐티클케어
- text: OPI.인피니트샤인.네일폴리쉬15ML.메니큐어.네일. - 08.인기 BEST ( 블렉계열컬러모음)_ISL W42(Best컬러) LotteOn
> 뷰티 > 네일 > 네일컬러 > 네일폴리쉬 LotteOn > 뷰티 > 네일 > 네일컬러 > 네일폴리쉬
- text: 오피아이 네일락커 컬러 매니큐어 R71 × 1개 LotteOn > 뷰티 > 헤어/바디 > 헤어스타일링 > 염색/매니큐어 LotteOn
> 뷰티 > 헤어/바디 > 헤어스타일링 > 염색/매니큐어
- text: 오피아이 인피니트 샤인2 매니큐어 C13 × 1개 LotteOn > 뷰티 > 헤어/바디 > 헤어스타일링 > 염색/매니큐어 LotteOn
> 뷰티 > 헤어/바디 > 헤어스타일링 > 염색/매니큐어
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: accuracy
value: 0.8433623980630115
name: Accuracy
---
# 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
<!-- - **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 |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 3 | <ul><li>'네일아트 네일 연장팁 8종 네일아트팁 네일연장 간편한네일아트 말랑네일팁 손톱연장 다이아몬드팁 (#M)SSG.COM/메이크업/메이크업세트 ssg > 뷰티 > 메이크업 > 메이크업세트'</li><li>'데싱디바 매직프레스 베르사유(OL) 2209 데싱디바 매직프레스 베르사유(OL) 2209 (#M)홈>네일>팁/스티커>네일 팁 OLIVEYOUNG > 네일 > 팁/스티커 > 네일 팁'</li><li>'나인펫홈앤리빙 네일팁 세트 31종 테이프글루 포함 NT-006 (#M)SSG.COM/메이크업/네일/네일케어용품 ssg > 뷰티 > 메이크업 > 네일'</li></ul> |
| 0 | <ul><li>'[OPI][리무버] 넌아세톤리무버 450ml ssg > 뷰티 > 메이크업 > 네일 ssg > 뷰티 > 메이크업 > 네일'</li><li>'포먼트 젤네일 O.3 라이트 살몬 × 1개 (#M)쿠팡 홈>뷰티>네일>젤네일>컬러 젤 Coupang > 뷰티 > 네일 > 젤네일 > 컬러 젤'</li><li>'미스터그린 스텐 큐티클 손톱깎이 MR1127 혼합색상 × 1개 (#M)쿠팡 홈>뷰티>네일>네일케어도구>클리퍼/푸셔/니퍼>클리퍼/손톱깎이 Coupang > 뷰티 > 네일 > 네일케어도구 > 클리퍼/푸셔/니퍼 > 클리퍼/손톱깎이'</li></ul> |
| 2 | <ul><li>'고양이 네일 케어 세트 족집게 + 손톱깎이 + 손톱줄 × 1세트 LotteOn > 뷰티 > 네일 > 네일케어소품 LotteOn > 뷰티 > 네일 > 네일케어소품'</li><li>'오피아이 OPI [세트상품] 핸드 큐티클 오일 TO GO2810443 2 (#M)SSG.COM/메이크업/네일/네일팁/네일스티커 ssg > 뷰티 > 메이크업 > 네일'</li><li>'네일샵 네일 패디케어 리클라이너 탁자 의자 스툴세트 전동리클라이닝173도+데크+대형스툴 LotteOn > 뷰티 > 뷰티기기 > 네일관리 LotteOn > 뷰티 > 뷰티기기 > 네일관리'</li></ul> |
| 1 | <ul><li>'뿌띠슈 컬러팡팡 네일 C13 뿌띠슈의요술봉 8ml × 1개 쿠팡 홈>어린이날>어린이화장품>네일케어;(#M)쿠팡 홈>뷰티>어린이화장품>네일케어 Coupang > 뷰티 > 어린이화장품 > 네일케어'</li><li>'에크레아)스킨핏브라탑 LARGE_BLACK SSG.COM/스포츠패션/용품/여성스포츠의류/트레이닝복상의;(#M)SSG.COM/헬스/요가/격투기/요가/필라테스 의류/요가복 상의 LOREAL > Ssg > 헬레나 루빈스타인 > Generic > 스킨'</li><li>'오피아이 네일 락커 매니큐어 15ml 옐로우(A65) × 1개 LotteOn > 뷰티 > 네일 > 네일스티커/네일팁 LotteOn > 뷰티 > 네일 > 네일스티커/네일팁'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.8434 |
## 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_top_bt2")
# Run inference
preds = model("네일스케치 2IN1 접착 홀로그램 필름 네일스티커 스톤 × 1개 LotteOn > 뷰티 > 네일 > 네일케어소품 LotteOn > 뷰티 > 네일 > 네일케어소품")
```
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### Out-of-Scope Use
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<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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### Recommendations
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 13 | 23.395 | 44 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 50 |
| 1 | 50 |
| 2 | 50 |
| 3 | 50 |
### Training Hyperparameters
- batch_size: (64, 64)
- num_epochs: (30, 30)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 100
- 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0032 | 1 | 0.411 | - |
| 0.1597 | 50 | 0.3995 | - |
| 0.3195 | 100 | 0.3502 | - |
| 0.4792 | 150 | 0.2877 | - |
| 0.6390 | 200 | 0.2325 | - |
| 0.7987 | 250 | 0.1729 | - |
| 0.9585 | 300 | 0.0879 | - |
| 1.1182 | 350 | 0.066 | - |
| 1.2780 | 400 | 0.0185 | - |
| 1.4377 | 450 | 0.0005 | - |
| 1.5974 | 500 | 0.0002 | - |
| 1.7572 | 550 | 0.0004 | - |
| 1.9169 | 600 | 0.0001 | - |
| 2.0767 | 650 | 0.0001 | - |
| 2.2364 | 700 | 0.0 | - |
| 2.3962 | 750 | 0.0002 | - |
| 2.5559 | 800 | 0.0001 | - |
| 2.7157 | 850 | 0.0003 | - |
| 2.8754 | 900 | 0.0001 | - |
| 3.0351 | 950 | 0.0 | - |
| 3.1949 | 1000 | 0.0001 | - |
| 3.3546 | 1050 | 0.0005 | - |
| 3.5144 | 1100 | 0.0005 | - |
| 3.6741 | 1150 | 0.0003 | - |
| 3.8339 | 1200 | 0.0002 | - |
| 3.9936 | 1250 | 0.0 | - |
| 4.1534 | 1300 | 0.0 | - |
| 4.3131 | 1350 | 0.0 | - |
| 4.4728 | 1400 | 0.0001 | - |
| 4.6326 | 1450 | 0.0004 | - |
| 4.7923 | 1500 | 0.0007 | - |
| 4.9521 | 1550 | 0.0001 | - |
| 5.1118 | 1600 | 0.0 | - |
| 5.2716 | 1650 | 0.0 | - |
| 5.4313 | 1700 | 0.0 | - |
| 5.5911 | 1750 | 0.0 | - |
| 5.7508 | 1800 | 0.0 | - |
| 5.9105 | 1850 | 0.0 | - |
| 6.0703 | 1900 | 0.0001 | - |
| 6.2300 | 1950 | 0.0001 | - |
| 6.3898 | 2000 | 0.0 | - |
| 6.5495 | 2050 | 0.0 | - |
| 6.7093 | 2100 | 0.0 | - |
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| 29.8722 | 9350 | 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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