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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: '[AKmall]입큰 셀피 HD 피니쉬 팩트 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 파우더 LotteOn > 뷰티
> 메이크업 > 베이스메이크업 > 파우더'
- text: 정샘물 스킨 세팅 스무딩/글로잉/톤밸런싱/톤코렉팅/워터선/톤업 선 베이스 모음전 스무딩 베이스 홈>💕기획세트;(#M)홈>썬케어 Naverstore
> 화장품/미용 > 베이스메이크업 > 메이크업베이스
- text: 에스쁘아 프로 테일러 비글로우 쿠션 올뉴 (본품+리필) 바닐라 MinSellAmount (#M)화장품/향수>베이스메이크업>쿠션/팩트
Gmarket > 뷰티 > 화장품/향수 > 베이스메이크업 > 쿠션/팩트
- text: 어반디케이 올나이트 울트라 글로우 세팅 픽서 118ml(건성) LOREAL > LotteOn > 어반디케이 > Branded > 어반디케이
LOREAL > LotteOn > 어반디케이 > Branded > 어반디케이
- text: Urban Decay All Nighter Long Lasting Setting Spray 4 oz 어반디케이 올 나이터 롱래스팅 픽서
118ml 1팩 (#M)쿠팡 홈>뷰티>메이크업>베이스 메이크업>메이크업픽서 Coupang > 뷰티 > 메이크업 > 베이스 메이크업 > 메이크업픽서
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: 0.8438958050005231
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:** 7 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 |
|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 6 | <ul><li>'쏘내추럴 올 데이 메이크업 픽서 143269 75ml × 2개 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 브러쉬 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 브러쉬'</li><li>'어네이즈 소프트 픽서 250ml (#M)쿠팡 홈>생활용품>헤어/바디/세안>스타일링/케어/세트>헤어스타일링>헤어스프레이 Coupang > 뷰티 > 헤어 > 헤어스타일링 > 헤어스프레이'</li><li>'쏘내추럴 올데이 타이트 메이크업 세팅 픽서 120ml LotteOn > 뷰티 > 스킨케어 > 미스트 LotteOn > 뷰티 > 스킨케어 > 미스트'</li></ul> |
| 2 | <ul><li>'1+1 더샘 커버 퍼펙트 팁 컨실러/더페이스샵 듀얼베일 컨실러 팁컨실러1.25호 라이트베이지_팁컨실러 컨투어베이지 (#M)홈>화장품/미용>베이스메이크업>컨실러 Naverstore > 화장품/미용 > 베이스메이크업 > 컨실러'</li><li>'더샘 커버 퍼펙션 아이디얼 컨실러 듀오 02호 리치베이지 02호 리치베이지 (#M)홈>화장품/미용>베이스메이크업>컨실러 Naverstore > 화장품/미용 > 베이스메이크업 > 컨실러'</li><li>'NEW 포에버 스킨 코렉트 00 LOREAL > DepartmentLotteOn > 메이블린 > Generic > 컨실러 LOREAL > DepartmentLotteOn > 메이블린 > Generic > 컨실러'</li></ul> |
| 5 | <ul><li>'코드글로컬러 픽스온 프라이머 톤 베이스 40ml(SPF33) (#M)화장품/미용>베이스메이크업>프라이머 Naverstore > 화장품/미용 > 베이스메이크업 > 프라이머'</li><li>'헤라 하이드레이팅 래디언스 프라이머 35ml (#M)위메프 > 뷰티 > 남성화장품 > 남성 메이크업 > 남성 베이스메이크업 위메프 > 뷰티 > 남성화장품 > 남성 메이크업 > 남성 베이스메이크업'</li><li>'베네피트 더 포어페셔널 하이드레이트 프라이머 22ml 포어페셔널 하이드레이트 44ml(파랑) (#M)홈>화장품/미용>베이스메이크업>프라이머 Naverstore > 화장품/미용 > 베이스메이크업 > 프라이머'</li></ul> |
| 0 | <ul><li>'[한스킨] 수퍼 라이트터치 비비크림 SPF30 30g 1.비비크림 1개 [GH990361] (#M)화장품/미용>베이스메이크업>BB크림 Naverstore > 화장품/미용 > 베이스메이크업 > BB크림'</li><li>'청미정 크랜베리 비비크림 (#M)위메프 > 뷰티 > 메이크업 > 베이스 메이크업 > BB크림 위메프 > 뷰티 > 메이크업 > 베이스 메이크업 > BB크림'</li><li>'프럼네이처 퍼펙트커버 비비크림 1호 라이트베이지 × 2개 (#M)쿠팡 홈>뷰티>메이크업>베이스 메이크업>BB/CC크림 Coupang > 뷰티 > 메이크업 > 베이스 메이크업 > BB/CC크림'</li></ul> |
| 4 | <ul><li>'[LIVE] 엉크르 드 뽀 쿠션&리필 스폐셜 세트 20호_5호 LOREAL > DepartmentLotteOn > 입생로랑 > Branded > 엉크르 드 뽀 쿠션 LOREAL > DepartmentLotteOn > 입생로랑 > Branded > 엉크르 드 뽀 쿠션'</li><li>'에뛰드하우스 더블 래스팅 세럼 파운데이션 30g 뉴트럴베이지 N04_1개 (#M)쿠팡 홈>뷰티>메이크업>베이스 메이크업>쿠션/팩트 Coupang > 뷰티 > 메이크업 > 베이스 메이크업 > 쿠션/팩트'</li><li>'[8월] 잉크쿠션 & 리필 세트 (+미니 잉크쿠션 증정) 30호_35호 LOREAL > DepartmentSsg > 입생로랑 > Branded > 엉크르 드 뽀 쿠션 LOREAL > DepartmentSsg > 입생로랑 > Branded > 엉크르 드 뽀 쿠션'</li></ul> |
| 1 | <ul><li>'샤넬 메이크업 베이스/ 샤넬 라 바즈 브라이트닝 메이크업 베이스/샤넬 복숭아 메베 로제 30ml SPF 40/PA+++/샤넬 쇼핑백 증정 (#M)홈>전체상품 Naverstore > 화장품/미용 > 베이스메이크업 > 메이크업베이스'</li><li>'더페이스샵 골드콜라겐 앰플 럭셔리 베이스 40ml LotteOn > 뷰티 > 베이스메이크업 > 메이크업베이스 LotteOn > 뷰티 > 베이스메이크업 > 메이크업베이스'</li><li>'어반디케이 올나이터 메이크업 픽서 스프레이 118ml MinSellAmount (#M)화장품/향수>베이스메이크업>파운데이션 Gmarket > 뷰티 > 화장품/향수 > 베이스메이크업 > 파운데이션'</li></ul> |
| 3 | <ul><li>'이니스프리 노세범 미네랄 파우더 5g 3개 (#M)홈>화장품/미용>베이스메이크업>파우더>루스파우더 Naverstore > 화장품/미용 > 베이스메이크업 > 파우더 > 루스파우더'</li><li>'메이크업포에버 UHD 세팅 파우더 & 퍼프 (+수분 프라이머 5ml) 2 바닐라 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 쿠션/팩트 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 쿠션/팩트'</li><li>' 노세범 미네랄 파우더 5g 8개 LotteOn > 뷰티 > 베이스메이크업 > 파우더 LotteOn > 뷰티 > 베이스메이크업 > 파우더'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.8439 |
## 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_bt5")
# Run inference
preds = model("[AKmall]입큰 셀피 HD 피니쉬 팩트 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 파우더 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 파우더")
```
<!--
### 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 | 23.4029 | 87 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 50 |
| 1 | 50 |
| 2 | 50 |
| 3 | 50 |
| 4 | 50 |
| 5 | 50 |
| 6 | 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 |
|:-------:|:-----:|:-------------:|:---------------:|
| 0.0018 | 1 | 0.4167 | - |
| 0.0914 | 50 | 0.4612 | - |
| 0.1828 | 100 | 0.4236 | - |
| 0.2742 | 150 | 0.3663 | - |
| 0.3656 | 200 | 0.2962 | - |
| 0.4570 | 250 | 0.23 | - |
| 0.5484 | 300 | 0.1439 | - |
| 0.6399 | 350 | 0.0941 | - |
| 0.7313 | 400 | 0.0609 | - |
| 0.8227 | 450 | 0.0421 | - |
| 0.9141 | 500 | 0.0244 | - |
| 1.0055 | 550 | 0.0076 | - |
| 1.0969 | 600 | 0.0018 | - |
| 1.1883 | 650 | 0.0013 | - |
| 1.2797 | 700 | 0.0009 | - |
| 1.3711 | 750 | 0.0007 | - |
| 1.4625 | 800 | 0.0005 | - |
| 1.5539 | 850 | 0.0004 | - |
| 1.6453 | 900 | 0.0003 | - |
| 1.7367 | 950 | 0.0004 | - |
| 1.8282 | 1000 | 0.0003 | - |
| 1.9196 | 1050 | 0.0003 | - |
| 2.0110 | 1100 | 0.0005 | - |
| 2.1024 | 1150 | 0.0003 | - |
| 2.1938 | 1200 | 0.0001 | - |
| 2.2852 | 1250 | 0.0001 | - |
| 2.3766 | 1300 | 0.0001 | - |
| 2.4680 | 1350 | 0.0001 | - |
| 2.5594 | 1400 | 0.0001 | - |
| 2.6508 | 1450 | 0.0001 | - |
| 2.7422 | 1500 | 0.0001 | - |
| 2.8336 | 1550 | 0.0001 | - |
| 2.9250 | 1600 | 0.0001 | - |
| 3.0165 | 1650 | 0.0 | - |
| 3.1079 | 1700 | 0.0 | - |
| 3.1993 | 1750 | 0.0 | - |
| 3.2907 | 1800 | 0.0 | - |
| 3.3821 | 1850 | 0.0 | - |
| 3.4735 | 1900 | 0.0 | - |
| 3.5649 | 1950 | 0.0 | - |
| 3.6563 | 2000 | 0.0 | - |
| 3.7477 | 2050 | 0.0 | - |
| 3.8391 | 2100 | 0.0 | - |
| 3.9305 | 2150 | 0.0 | - |
| 4.0219 | 2200 | 0.0 | - |
| 4.1133 | 2250 | 0.0 | - |
| 4.2048 | 2300 | 0.0 | - |
| 4.2962 | 2350 | 0.0 | - |
| 4.3876 | 2400 | 0.0 | - |
| 4.4790 | 2450 | 0.0 | - |
| 4.5704 | 2500 | 0.0 | - |
| 4.6618 | 2550 | 0.0 | - |
| 4.7532 | 2600 | 0.0 | - |
| 4.8446 | 2650 | 0.0 | - |
| 4.9360 | 2700 | 0.0 | - |
| 5.0274 | 2750 | 0.0 | - |
| 5.1188 | 2800 | 0.0 | - |
| 5.2102 | 2850 | 0.0 | - |
| 5.3016 | 2900 | 0.0 | - |
| 5.3931 | 2950 | 0.0 | - |
| 5.4845 | 3000 | 0.0 | - |
| 5.5759 | 3050 | 0.0 | - |
| 5.6673 | 3100 | 0.0 | - |
| 5.7587 | 3150 | 0.0 | - |
| 5.8501 | 3200 | 0.0 | - |
| 5.9415 | 3250 | 0.0 | - |
| 6.0329 | 3300 | 0.0 | - |
| 6.1243 | 3350 | 0.0 | - |
| 6.2157 | 3400 | 0.0 | - |
| 6.3071 | 3450 | 0.0 | - |
| 6.3985 | 3500 | 0.0 | - |
| 6.4899 | 3550 | 0.0207 | - |
| 6.5814 | 3600 | 0.0203 | - |
| 6.6728 | 3650 | 0.0015 | - |
| 6.7642 | 3700 | 0.0001 | - |
| 6.8556 | 3750 | 0.0 | - |
| 6.9470 | 3800 | 0.0 | - |
| 7.0384 | 3850 | 0.0 | - |
| 7.1298 | 3900 | 0.0 | - |
| 7.2212 | 3950 | 0.0 | - |
| 7.3126 | 4000 | 0.0 | - |
| 7.4040 | 4050 | 0.0 | - |
| 7.4954 | 4100 | 0.0 | - |
| 7.5868 | 4150 | 0.0 | - |
| 7.6782 | 4200 | 0.0 | - |
| 7.7697 | 4250 | 0.0 | - |
| 7.8611 | 4300 | 0.0 | - |
| 7.9525 | 4350 | 0.0 | - |
| 8.0439 | 4400 | 0.0 | - |
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| 8.3181 | 4550 | 0.0 | - |
| 8.4095 | 4600 | 0.0 | - |
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| 8.5923 | 4700 | 0.0 | - |
| 8.6837 | 4750 | 0.0 | - |
| 8.7751 | 4800 | 0.0 | - |
| 8.8665 | 4850 | 0.0 | - |
| 8.9580 | 4900 | 0.0 | - |
| 9.0494 | 4950 | 0.0 | - |
| 9.1408 | 5000 | 0.0 | - |
| 9.2322 | 5050 | 0.0 | - |
| 9.3236 | 5100 | 0.0 | - |
| 9.4150 | 5150 | 0.0 | - |
| 9.5064 | 5200 | 0.0 | - |
| 9.5978 | 5250 | 0.0 | - |
| 9.6892 | 5300 | 0.0 | - |
| 9.7806 | 5350 | 0.0 | - |
| 9.8720 | 5400 | 0.0 | - |
| 9.9634 | 5450 | 0.0 | - |
| 10.0548 | 5500 | 0.0 | - |
| 10.1463 | 5550 | 0.0 | - |
| 10.2377 | 5600 | 0.0 | - |
| 10.3291 | 5650 | 0.0 | - |
| 10.4205 | 5700 | 0.0 | - |
| 10.5119 | 5750 | 0.0 | - |
| 10.6033 | 5800 | 0.0 | - |
| 10.6947 | 5850 | 0.0 | - |
| 10.7861 | 5900 | 0.0 | - |
| 10.8775 | 5950 | 0.0 | - |
| 10.9689 | 6000 | 0.0 | - |
| 11.0603 | 6050 | 0.0 | - |
| 11.1517 | 6100 | 0.0 | - |
| 11.2431 | 6150 | 0.0 | - |
| 11.3346 | 6200 | 0.0 | - |
| 11.4260 | 6250 | 0.0 | - |
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### 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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