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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: '[시세이도] NEW 싱크로 스킨 래디언트 리프팅 파운데이션 SPF30/PA++++ 30ml 130 오팔 (#M)홈>메이크업>베이스메이크업
HMALL > 뷰티 > 메이크업 > 베이스메이크업'
- text: 어뮤즈 메타 픽싱 비건 쿠션 리필 (3종 택 1) 02 누드 (#M)홈>화장품/미용>베이스메이크업>파운데이션>쿠션형 Naverstore
> 화장품/미용 > 베이스메이크업 > 파운데이션 > 쿠션형
- text: 에스쁘아 프로 테일러 파운데이션 비 글로우 30ml MinSellAmount (#M)화장품/향수>베이스메이크업>파운데이션 Gmarket
> 뷰티 > 화장품/향수 > 베이스메이크업 > 파운데이션
- text: (현대백화점) 톰 포드 뷰티 셰이드 앤 일루미네이트 소프트 래디언스 파운데이션 SPF50/PA++++ 0.4 로즈 (#M)화장품/향수>베이스메이크업>파운데이션
Gmarket > 뷰티 > 화장품/향수 > 베이스메이크업 > 파운데이션
- text: '[정샘물] 마스터클래스 래디언트 쿠션(리필포함)(+코렉팅 베이스5mlx3개)(강남점) N1아이보리 (#M)11st>메이크업>페이스메이크업>파운데이션
11st > 뷰티 > 메이크업 > 페이스메이크업 > 파운데이션'
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: 0.9475307038057129
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:** 5 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 |
|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 2 | <ul><li>'에스쁘아 프로테일러 비글로우 스틱 파운데이션 13g 23호베이지 (#M)홈>화장품/미용>베이스메이크업>파운데이션>스틱형 Naverstore > 화장품/미용 > 베이스메이크업 > 파운데이션 > 스틱형'</li><li>'그라펜 에어커버 스틱 파운데이션 23호 베이지 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 파운데이션 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 파운데이션'</li><li>'바비 브라운 스킨 파운데이션 스틱-2.5 원 샌드 9g (#M)화장품/미용>베이스메이크업>파운데이션>크림형 Naverstore > 화장품/미용 > 베이스메이크업 > 파운데이션 > 크림형'</li></ul> |
| 1 | <ul><li>'정샘물 스킨 세팅 톤 코렉팅 베이스 40ml 글로잉 베이스 (#M)11st>메이크업>페이스메이크업>메이크업베이스 11st > 뷰티 > 메이크업 > 페이스메이크업 > 메이크업베이스'</li><li>'아이오페 퍼펙트 커버 메이크업베이스 35ml 2호 라이트퍼플 × 3개 (#M)쿠팡 홈>뷰티>메이크업>베이스 메이크업>베이스/프라이머 Coupang > 뷰티 > 메이크업 > 베이스 메이크업 > 베이스/프라이머'</li><li>'아이오페 퍼펙트 커버 베이스 35ml 2호-퍼플 (#M)홈>화장품/미용>베이스메이크업>메이크업베이스 Naverstore > 화장품/미용 > 베이스메이크업 > 메이크업베이스'</li></ul> |
| 0 | <ul><li>'헤라 글로우 래스팅 파운데이션 17C1 페탈 아이보리 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 베이스/프라이머 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 베이스/프라이머'</li><li>'[에스티 로더] 더블웨어 파운데이션 30ml SPF 10/PA++ (+프라이머 정품 ) 1W0 웜 포슬린 홈>기획 세트;홈>더블웨어;홈>더블 웨어;화장품/미용>베이스메이크업>파운데이션>리퀴드형;(#M)홈>전체상품 Naverstore > 베이스메이크업 > 파운데이션'</li><li>'에스쁘아 프로테일러 파운데이션 비 글로우 10ml 4호 베이지 × 1개 (#M)쿠팡 홈>뷰티>메이크업>베이스 메이크업>파운데이션 Coupang > 뷰티 > 로드샵 > 메이크업 > 베이스 메이크업 > 파운데이션'</li></ul> |
| 4 | <ul><li>'시세이도 스포츠 커버 파운데이션 20g S101 (#M)홈>화장품/미용>베이스메이크업>파운데이션>크림형 Naverstore > 화장품/미용 > 베이스메이크업 > 파운데이션 > 크림형'</li><li>'시세이도 스포츠 커버 파운데이션 20g S100 × 1개 Coupang > 뷰티 > 메이크업 > 베이스 메이크업 > 파운데이션;(#M)쿠팡 홈>뷰티>메이크업>베이스 메이크업>파운데이션 Coupang > 뷰티 > 메이크업 > 베이스 메이크업 > 파운데이션'</li><li>'에이지투웨니스 오리지날 샤이닝드롭 케이스+리필3개 (+커피쿠폰+폼20ml) 샤이닝드롭(화이트)23호케이스+리필3개_폼20ml (#M)화장품/미용>베이스메이크업>파운데이션>쿠션형 AD > Naverstore > 화장품/미용 > 베이스메이크업 > 파운데이션 > 크림형'</li></ul> |
| 3 | <ul><li>'매트 벨벳 스킨 컴팩트 스폰지 단품없음 LotteOn > 뷰티 > 뷰티기기 > 액세서리/소모품 LotteOn > 뷰티 > 뷰티기기 > 액세서리/소모품'</li><li>'[BF적립] 엉크르 드 뽀 쿠션&리필 세트(+스탠딩 미러+5천LPOINT) 20호_15호 LOREAL > DepartmentLotteOn > 입생로랑 > Branded > 입생로랑 LOREAL > DepartmentLotteOn > 입생로랑 > Branded > 입생로랑'</li><li>'코튼 LotteOn > 뷰티 > 뷰티기기 > 액세서리/소모품 LotteOn > 뷰티 > 뷰티기기 > 액세서리/소모품'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9475 |
## 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_top_bt5_4")
# Run inference
preds = model("[시세이도] NEW 싱크로 스킨 래디언트 리프팅 파운데이션 SPF30/PA++++ 30ml 130 오팔 (#M)홈>메이크업>베이스메이크업 HMALL > 뷰티 > 메이크업 > 베이스메이크업")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## 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.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 12 | 22.928 | 52 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 50 |
| 1 | 50 |
| 2 | 50 |
| 3 | 50 |
| 4 | 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 |
|:-------:|:-----:|:-------------:|:---------------:|
| 0.0026 | 1 | 0.521 | - |
| 0.1279 | 50 | 0.4636 | - |
| 0.2558 | 100 | 0.42 | - |
| 0.3836 | 150 | 0.292 | - |
| 0.5115 | 200 | 0.1539 | - |
| 0.6394 | 250 | 0.0626 | - |
| 0.7673 | 300 | 0.0343 | - |
| 0.8951 | 350 | 0.0071 | - |
| 1.0230 | 400 | 0.0023 | - |
| 1.1509 | 450 | 0.0005 | - |
| 1.2788 | 500 | 0.0006 | - |
| 1.4066 | 550 | 0.0003 | - |
| 1.5345 | 600 | 0.0002 | - |
| 1.6624 | 650 | 0.0001 | - |
| 1.7903 | 700 | 0.0002 | - |
| 1.9182 | 750 | 0.0006 | - |
| 2.0460 | 800 | 0.0002 | - |
| 2.1739 | 850 | 0.0001 | - |
| 2.3018 | 900 | 0.0 | - |
| 2.4297 | 950 | 0.0 | - |
| 2.5575 | 1000 | 0.0 | - |
| 2.6854 | 1050 | 0.0 | - |
| 2.8133 | 1100 | 0.0 | - |
| 2.9412 | 1150 | 0.0 | - |
| 3.0691 | 1200 | 0.0 | - |
| 3.1969 | 1250 | 0.0 | - |
| 3.3248 | 1300 | 0.0 | - |
| 3.4527 | 1350 | 0.0007 | - |
| 3.5806 | 1400 | 0.0005 | - |
| 3.7084 | 1450 | 0.0009 | - |
| 3.8363 | 1500 | 0.0008 | - |
| 3.9642 | 1550 | 0.0003 | - |
| 4.0921 | 1600 | 0.0002 | - |
| 4.2199 | 1650 | 0.0 | - |
| 4.3478 | 1700 | 0.0 | - |
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| 4.6036 | 1800 | 0.0 | - |
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| 6.0102 | 2350 | 0.0001 | - |
| 6.1381 | 2400 | 0.0006 | - |
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| 21.7391 | 8500 | 0.0 | - |
| 21.8670 | 8550 | 0.0 | - |
| 21.9949 | 8600 | 0.0 | - |
| 22.1228 | 8650 | 0.0 | - |
| 22.2506 | 8700 | 0.0 | - |
| 22.3785 | 8750 | 0.0 | - |
| 22.5064 | 8800 | 0.0 | - |
| 22.6343 | 8850 | 0.0 | - |
| 22.7621 | 8900 | 0.0 | - |
| 22.8900 | 8950 | 0.0 | - |
| 23.0179 | 9000 | 0.0 | - |
| 23.1458 | 9050 | 0.0 | - |
| 23.2737 | 9100 | 0.0 | - |
| 23.4015 | 9150 | 0.0 | - |
| 23.5294 | 9200 | 0.0 | - |
| 23.6573 | 9250 | 0.0 | - |
| 23.7852 | 9300 | 0.0 | - |
| 23.9130 | 9350 | 0.0 | - |
| 24.0409 | 9400 | 0.0 | - |
| 24.1688 | 9450 | 0.0 | - |
| 24.2967 | 9500 | 0.0 | - |
| 24.4246 | 9550 | 0.0 | - |
| 24.5524 | 9600 | 0.0 | - |
| 24.6803 | 9650 | 0.0 | - |
| 24.8082 | 9700 | 0.0 | - |
| 24.9361 | 9750 | 0.0 | - |
| 25.0639 | 9800 | 0.0 | - |
| 25.1918 | 9850 | 0.0 | - |
| 25.3197 | 9900 | 0.0 | - |
| 25.4476 | 9950 | 0.0 | - |
| 25.5754 | 10000 | 0.0 | - |
| 25.7033 | 10050 | 0.0 | - |
| 25.8312 | 10100 | 0.0 | - |
| 25.9591 | 10150 | 0.0 | - |
| 26.0870 | 10200 | 0.0 | - |
| 26.2148 | 10250 | 0.0 | - |
| 26.3427 | 10300 | 0.0 | - |
| 26.4706 | 10350 | 0.0 | - |
| 26.5985 | 10400 | 0.0 | - |
| 26.7263 | 10450 | 0.0 | - |
| 26.8542 | 10500 | 0.0 | - |
| 26.9821 | 10550 | 0.0 | - |
| 27.1100 | 10600 | 0.0 | - |
| 27.2379 | 10650 | 0.0 | - |
| 27.3657 | 10700 | 0.0 | - |
| 27.4936 | 10750 | 0.0 | - |
| 27.6215 | 10800 | 0.0 | - |
| 27.7494 | 10850 | 0.0 | - |
| 27.8772 | 10900 | 0.0 | - |
| 28.0051 | 10950 | 0.0 | - |
| 28.1330 | 11000 | 0.0 | - |
| 28.2609 | 11050 | 0.0 | - |
| 28.3887 | 11100 | 0.0 | - |
| 28.5166 | 11150 | 0.0 | - |
| 28.6445 | 11200 | 0.0 | - |
| 28.7724 | 11250 | 0.0 | - |
| 28.9003 | 11300 | 0.0 | - |
| 29.0281 | 11350 | 0.0 | - |
| 29.1560 | 11400 | 0.0 | - |
| 29.2839 | 11450 | 0.0 | - |
| 29.4118 | 11500 | 0.0 | - |
| 29.5396 | 11550 | 0.0 | - |
| 29.6675 | 11600 | 0.0 | - |
| 29.7954 | 11650 | 0.0 | - |
| 29.9233 | 11700 | 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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