---
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: 트위저맨 포인트 트위저 Pretty in Pink (#M)홈>화장품/미용>뷰티소품>페이스소품>기타페이스소품 Naverstore > 화장품/미용
> 뷰티소품 > 페이스소품 > 기타페이스소품
- text: 에스쁘아 에어 퍼프 5개입 소프트 터치 에어퍼프 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 퍼프 LotteOn >
뷰티 > 뷰티기기/소품 > 메이크업소품 > 브러쉬
- text: 더툴랩 더스타일 래쉬 - 리얼(TSL001) x 1개 리얼(TSL001) × 1개 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품
> 속눈썹관리 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품 > 속눈썹관리
- text: 미용재료/셀프파마/롯드/헤어롤/미용용품/파지/귀마개/염색볼/집게핀/샤워캡/헤어밴드 41.다용도 공병 2개 홈>펌,염색,미용소도구;홈>파마용품;(#M)홈>파마
소도구>파마용품 Naverstore > 화장품/미용 > 뷰티소품 > 헤어소품 > 기타헤어소품
- text: 에스쁘아 비글로우 에어 퍼프 5개입(22AD) (#M)홈>화장품/미용>뷰티소품>페이스소품>기타페이스소품 Naverstore > 화장품/미용
> 뷰티소품 > 페이스소품 > 기타페이스소품
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: 0.9419292632686155
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:** 8 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 |
|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 7 | <ul><li>'[JAJU/자주] 원형 리필 공병 통 110ml ssg > 뷰티 > 미용기기/소품 > 거울/용기/기타소품;ssg > 뷰티 > 헤어/바디/미용/구강 > 미용기기 ssg > 뷰티 > 미용기기/소품 > 거울/용기/기타소품'</li><li>'세맘스 아기랑 + 엄마랑 파우치 세트 핑크스마일_엄마(가로 11.5cm x 세로 13cm), 아기(가로 8cm x 세로 10.5cm) (#M)쿠팡 홈>여행용품>여행파우치>화장품파우치 Coupang > 뷰티 > 뷰티소품 > 용기/거울/기타소품 > 파우치'</li><li>'라인 프린팅 파스텔컬러 롤온공병 10ml 6종 세트 흰색(뚜껑) × 1세트 (#M)쿠팡 홈>뷰티>뷰티소품>용기/거울/기타소품>기타소품 Coupang > 뷰티 > 뷰티소품 > 용기/거울/기타소품 > 기타소품'</li></ul> |
| 3 | <ul><li>'트위저맨 슬랜트 트위저 족집게 베이비 핑크 × 9개 (#M)쿠팡 홈>뷰티>뷰티소품>아이소품>족집게/샤프너 Coupang > 뷰티 > 뷰티소품 > 아이소품 > 족집게/샤프너'</li><li>'트위저맨 미니 슬랜트 트위저 로즈골드 265161 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 파운데이션 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 파운데이션'</li><li>'트위저맨 클래식 슬랜트 트위저 베이비핑크, 1개 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 브러쉬 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 브러쉬'</li></ul> |
| 6 | <ul><li>'천일 매직 롯드 10P 1호~6호 뿌리볼륨롯드 파마롯드 매직롯드 5호_1개 홈>화장품/미용>뷰티소품>헤어소품>헤어롤;홈>전체상품;(#M)홈>롯드 Naverstore > 화장품/미용 > 뷰티소품 > 헤어소품 > 헤어롤'</li><li>'다이슨 45mm 35mm 롤브러쉬 대왕롤빗 엉킴방지빗 니켈블랙 (#M)홈>미용건강 Naverstore > 화장품/미용 > 뷰티소품 > 헤어소품 > 헤어브러시'</li><li>'프리시전 섀이더 브러쉬 스몰 단품없음 LotteOn > 뷰티 > 뷰티기기 > 액세서리/소모품 LotteOn > 뷰티 > 뷰티기기 > 액세서리/소모품'</li></ul> |
| 0 | <ul><li>'천연 자초 립밤 만들기 키트 diy 향 선택(8개) 사과+에탄올20ml (#M)홈>비누&립밤&세제 만들기>만들기키트 Naverstore > 화장품/미용 > 색조메이크업 > 립케어'</li></ul> |
| 5 | <ul><li>'프로 피니쉬 스폰지 단품없음 LotteOn > 뷰티 > 뷰티기기 > 액세서리/소모품 LotteOn > 뷰티 > 뷰티기기 > 액세서리/소모품'</li><li>'JAJU 사각 면봉_화장 겸용 200P 기타_FR LotteOn > 뷰티 > 뷰티기기/소품 > 위생용품 > 면봉 LotteOn > 뷰티 > 뷰티기기/소품 > 위생용품 > 면봉'</li><li>'mts 롤러 기계 MTS 스탬프 앰플 바르는 도구 니들 빠른흡수 상품선택_2-더마롤러-0.3mm LotteOn > 뷰티 > 뷰티기기/소품 > 피부케어기 > 피부케어기 LotteOn > 뷰티 > 뷰티기기/소품 > 피부케어기 > 피부케어기'</li></ul> |
| 1 | <ul><li>'더툴랩 101B 베이비태스커 파운데이션 베이스 메이크업 브러쉬 쿠션브러쉬 236097 (#M)홈>화장품/미용>뷰티소품>메이크업브러시>브러시세트 Naverstore > 화장품/미용 > 뷰티소품 > 메이크업브러시 > 브러시세트'</li><li>'더툴랩 204 블렌딩 아이섀도우 스몰 총알 브러쉬 (#M)화장품/미용>뷰티소품>페이스소품>코털제거기 AD > Naverstore > 화장품/미용 > 뷰티소품 > 페이스소품 > 코털제거기'</li><li>'더툴랩 브러쉬 231 컨실러 파운데이션 (#M)화장품/미용>뷰티소품>메이크업브러시>페이스브러시 LO > Naverstore > 화장품/미용 > 뷰티소품 > 메이크업브러시 > 페이스브러시'</li></ul> |
| 2 | <ul><li>'요들가운 미용실 LC 커트보 어깨보 컷트보 인쇄가능 15.모델210T커트보_블랙 (#M)홈>가운,유니폼>컷트보 Naverstore > 화장품/미용 > 뷰티소품 > 헤어소품 > 기타헤어소품'</li><li>'요들가운 미용실 LC 커트보 어깨보 컷트보 인쇄가능 12.듀스포체크 커트보_퍼플 (#M)홈>가운,유니폼>컷트보 Naverstore > 화장품/미용 > 뷰티소품 > 헤어소품 > 기타헤어소품'</li><li>'[백화점][JPClarisse] 장폴클라리쎄 거미 왕대 집게핀 JPSA0001 진베이지 (#M)GSSHOP>뷰티>뷰티소품>헤어소품 GSSHOP > 뷰티 > 뷰티소품 > 헤어소품 > 헤어집게'</li></ul> |
| 4 | <ul><li>'레터링 쇄골 현아 타투 스티커 30장 마스크 판박이 3타투세트30장-수채화 LotteOn > 뷰티 > 마스크/팩 > 기타패치 LotteOn > 뷰티 > 마스크/팩 > 기타패치'</li><li>'산리오 캐릭터 타투 스티커 어린이 문신 마스크판박이 5.헬로키티(2매입) 홈>패션잡화🛍>잡화🐱\u200d💻;(#M)홈>캐릭터🙂>산리오 Naverstore > 화장품/미용 > 뷰티소품 > 타투'</li><li>'문신 타투 스티커 바디 형 쇄골 반팔 레터링 흉터 커버__개성 다이소 헤나 다목적 노출 패션 미용 다용도 추천 패셔니스타 여름 A type 타투스티커 30종세트 (#M)SSG.COM/헤어/바디/슬리밍/푸드/기타용품/타투 ssg > 뷰티 > 헤어/바디 > 슬리밍/푸드/기타용품 > 타투'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9419 |
## 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_bt6")
# Run inference
preds = model("에스쁘아 에어 퍼프 5개입 소프트 터치 에어퍼프 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 퍼프 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 브러쉬")
```
<!--
### 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 | 22.0313 | 72 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 1 |
| 1 | 50 |
| 2 | 50 |
| 3 | 50 |
| 4 | 50 |
| 5 | 50 |
| 6 | 50 |
| 7 | 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 | 0.4099 | - |
| 0.0911 | 50 | 0.3973 | - |
| 0.1821 | 100 | 0.3456 | - |
| 0.2732 | 150 | 0.2947 | - |
| 0.3643 | 200 | 0.2369 | - |
| 0.4554 | 250 | 0.1705 | - |
| 0.5464 | 300 | 0.107 | - |
| 0.6375 | 350 | 0.0696 | - |
| 0.7286 | 400 | 0.0494 | - |
| 0.8197 | 450 | 0.0488 | - |
| 0.9107 | 500 | 0.0307 | - |
| 1.0018 | 550 | 0.0259 | - |
| 1.0929 | 600 | 0.0247 | - |
| 1.1840 | 650 | 0.022 | - |
| 1.2750 | 700 | 0.0215 | - |
| 1.3661 | 750 | 0.005 | - |
| 1.4572 | 800 | 0.0007 | - |
| 1.5483 | 850 | 0.0004 | - |
| 1.6393 | 900 | 0.0002 | - |
| 1.7304 | 950 | 0.0001 | - |
| 1.8215 | 1000 | 0.0001 | - |
| 1.9126 | 1050 | 0.0001 | - |
| 2.0036 | 1100 | 0.0001 | - |
| 2.0947 | 1150 | 0.0001 | - |
| 2.1858 | 1200 | 0.0001 | - |
| 2.2769 | 1250 | 0.0 | - |
| 2.3679 | 1300 | 0.0 | - |
| 2.4590 | 1350 | 0.0 | - |
| 2.5501 | 1400 | 0.0 | - |
| 2.6412 | 1450 | 0.0 | - |
| 2.7322 | 1500 | 0.0 | - |
| 2.8233 | 1550 | 0.0 | - |
| 2.9144 | 1600 | 0.0 | - |
| 3.0055 | 1650 | 0.0 | - |
| 3.0965 | 1700 | 0.0 | - |
| 3.1876 | 1750 | 0.0 | - |
| 3.2787 | 1800 | 0.0 | - |
| 3.3698 | 1850 | 0.0 | - |
| 3.4608 | 1900 | 0.0 | - |
| 3.5519 | 1950 | 0.0 | - |
| 3.6430 | 2000 | 0.0 | - |
| 3.7341 | 2050 | 0.0 | - |
| 3.8251 | 2100 | 0.0 | - |
| 3.9162 | 2150 | 0.0 | - |
| 4.0073 | 2200 | 0.0 | - |
| 4.0984 | 2250 | 0.0 | - |
| 4.1894 | 2300 | 0.0 | - |
| 4.2805 | 2350 | 0.0 | - |
| 4.3716 | 2400 | 0.0 | - |
| 4.4627 | 2450 | 0.0 | - |
| 4.5537 | 2500 | 0.0 | - |
| 4.6448 | 2550 | 0.0 | - |
| 4.7359 | 2600 | 0.0 | - |
| 4.8270 | 2650 | 0.0 | - |
| 4.9180 | 2700 | 0.0 | - |
| 5.0091 | 2750 | 0.0 | - |
| 5.1002 | 2800 | 0.0 | - |
| 5.1913 | 2850 | 0.0 | - |
| 5.2823 | 2900 | 0.0 | - |
| 5.3734 | 2950 | 0.0 | - |
| 5.4645 | 3000 | 0.0 | - |
| 5.5556 | 3050 | 0.0 | - |
| 5.6466 | 3100 | 0.0 | - |
| 5.7377 | 3150 | 0.0 | - |
| 5.8288 | 3200 | 0.0 | - |
| 5.9199 | 3250 | 0.0 | - |
| 6.0109 | 3300 | 0.0 | - |
| 6.1020 | 3350 | 0.0 | - |
| 6.1931 | 3400 | 0.0 | - |
| 6.2842 | 3450 | 0.0 | - |
| 6.3752 | 3500 | 0.0 | - |
| 6.4663 | 3550 | 0.0 | - |
| 6.5574 | 3600 | 0.0 | - |
| 6.6485 | 3650 | 0.0 | - |
| 6.7395 | 3700 | 0.0 | - |
| 6.8306 | 3750 | 0.0 | - |
| 6.9217 | 3800 | 0.0 | - |
| 7.0128 | 3850 | 0.0 | - |
| 7.1038 | 3900 | 0.0 | - |
| 7.1949 | 3950 | 0.0 | - |
| 7.2860 | 4000 | 0.0 | - |
| 7.3770 | 4050 | 0.0 | - |
| 7.4681 | 4100 | 0.0 | - |
| 7.5592 | 4150 | 0.0 | - |
| 7.6503 | 4200 | 0.0 | - |
| 7.7413 | 4250 | 0.0 | - |
| 7.8324 | 4300 | 0.0 | - |
| 7.9235 | 4350 | 0.0 | - |
| 8.0146 | 4400 | 0.0 | - |
| 8.1056 | 4450 | 0.0 | - |
| 8.1967 | 4500 | 0.0 | - |
| 8.2878 | 4550 | 0.0 | - |
| 8.3789 | 4600 | 0.0 | - |
| 8.4699 | 4650 | 0.0 | - |
| 8.5610 | 4700 | 0.0 | - |
| 8.6521 | 4750 | 0.0 | - |
| 8.7432 | 4800 | 0.0 | - |
| 8.8342 | 4850 | 0.0 | - |
| 8.9253 | 4900 | 0.0 | - |
| 9.0164 | 4950 | 0.0 | - |
| 9.1075 | 5000 | 0.0 | - |
| 9.1985 | 5050 | 0.0 | - |
| 9.2896 | 5100 | 0.0 | - |
| 9.3807 | 5150 | 0.0 | - |
| 9.4718 | 5200 | 0.0 | - |
| 9.5628 | 5250 | 0.0 | - |
| 9.6539 | 5300 | 0.0 | - |
| 9.7450 | 5350 | 0.0 | - |
| 9.8361 | 5400 | 0.0 | - |
| 9.9271 | 5450 | 0.0 | - |
| 10.0182 | 5500 | 0.0 | - |
| 10.1093 | 5550 | 0.0 | - |
| 10.2004 | 5600 | 0.0 | - |
| 10.2914 | 5650 | 0.0 | - |
| 10.3825 | 5700 | 0.0 | - |
| 10.4736 | 5750 | 0.0 | - |
| 10.5647 | 5800 | 0.0 | - |
| 10.6557 | 5850 | 0.0 | - |
| 10.7468 | 5900 | 0.0 | - |
| 10.8379 | 5950 | 0.0 | - |
| 10.9290 | 6000 | 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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