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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: 7 GRAB ME 레드 생로랑 Volupte 리퀴드 컬러 Balm 풀사이즈 B 20대여자 옵션없음 남인터내셔널
- text: 힐러랩 울트라본드 케라틴 단백질 트리트먼트 500ml 옵션없음 주식회사 와이제이비앤
- text: 바이오더마 센시비오 클렌징밀크 250ml 옵션없음 주식회사 하이유로
- text: 에스테티카 데미지 케어 컨센트레이트120ml /헤어오일 에센스 세럼 옵션없음 주식회사 베로유코스메틱
- text: 브이티코스메틱 VT 리들샷 700 시너지리페어 크림 옵션없음 북극곰마켓
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: 0.7873765467135884
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:** 13 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 |
|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1.0 | <ul><li>'태닝키티파츠 TKT-02-08 썬탠키티 5개입 탄 갸루 하와이 비키니 태닝키티파츠 TKT-02-01 5개입 임프주식회사'</li><li>'지비츠 네일아트 회전 스피너 파츠 부자재 네일 베어링 2.5 사이즈 띠부'</li><li>'루핀 젤클리너 젤리무버 500ml 아세톤 젤클렌져 루핀젤리무버500ml 신나라닷컴'</li></ul> |
| 7.0 | <ul><li>'마담미쉘 알로베라 퓨어 겔 400ml 알로에 99% 수딩젤 옵션없음 달이커머스'</li><li>'에콜린 비건 밀크 선스크린 50 ml [단품] 비건밀크 선스크린 (18900원) 오가니아(주)'</li><li>'비욘드 엔젤 아쿠아 보습 장벽 선퀴드 50ml_비건 옵션없음 빈티지브릿지'</li></ul> |
| 12.0 | <ul><li>'[르네휘테르]나뚜리아 드라이 샴푸 75ml 나뚜리아 드라이 샴푸 75ml 이로븐'</li><li>'탑스칼프 헤어토닉 120ml 두피 클리닉 탑스칼프 탈모샴푸 300ml 주식회사휴웰'</li><li>'케라스타즈 뉴트리티브 마스퀸텐스 리슈 200ml 3474637155001 옵션없음 퓨쳐 디엠 (FUTURE DIEM)'</li></ul> |
| 2.0 | <ul><li>'아나프노 근육 통증 스포츠 온열 찜질 마사지 크림 100ml 아나프노크림 100ml x 1개 케이제이미디어'</li><li>'참존 콘트롤 크림 셀프 마사지 150ml 1개 옵션없음 주식회사 리플레이'</li><li>'리플로우 파워샷 고주파 마사지 크림 옵션없음 호원'</li></ul> |
| 8.0 | <ul><li>'가히 멀티밤 CV 본품 9g 가히 멀티밤 CV 본품 1개 ㈜코리아테크'</li><li>'프레티 바이옴 콜라겐 아이크림 30ml 20개 옵션없음 건강드림'</li><li>'르네셀 멀티 펩타이드 토너(재고정리) 옵션없음 숙이네 잡화'</li></ul> |
| 6.0 | <ul><li>'1/1+1 스틸 마스카라 내추럴 롱래쉬 볼륨 워터프루프 메탈 마스카라 01 블랙x2 와이우'</li><li>'딸리까 리포실 압솔뤼 10ml 옵션없음 스루치로 유한책임회사'</li><li>'디올 어딕트 립 맥시마이저 020 마호가니 옵션없음 스터닝헌팅'</li></ul> |
| 0.0 | <ul><li>'비비드쏘울 위치하젤 젠틀 카밍 앰플 마스크 119 10매 비비드쏘울'</li><li>'알롱 인티메이트 클린저 남성용 청결제 200ml 4개 옵션없음 건강드림'</li><li>'꽃을든남자 이모션 스킨 옵션없음 테디코스'</li></ul> |
| 4.0 | <ul><li>'AHC 아우라 시크릿 톤업 벨벳 35g+10g 2개 AOD_01)35g+10g 2개+쇼핑백 (주)카버코리아'</li><li>'KANEBO (가네보) 라이브 리스킨 웨어 핑크 오크르 B1개 (x1) 옵션없음 HaruHaru'</li><li>'기획상품 애리조 커버 TV스틱 올 추천템 No.5 다크 베이지 핑쇼59'</li></ul> |
| 9.0 | <ul><li>'바이오뷰텍 바이오옵틱스 아이크린 리드 클리너 30매 1022127 옵션없음 굿데이'</li><li>'휴대용 종이비누 33종 (챠리, 큐리아, 효겐, 크럭스) 17_효겐 동물 친구들 피코스 드 코레아'</li><li>'식물나라 제주 탄산수 퀵 립 앤 아이 리무버 300ml 옵션없음 밀레니엄'</li></ul> |
| 10.0 | <ul><li>'장폴고티에 르말 엘릭서 퍼퓸 200ML 옵션없음 비즈앤플랜 주식회사'</li><li>'인센스홀더향 향꽂이 홀더 물방울 인테리어 인센스 (WD2F3FF) 본상품선택 기타/해당사항 없음'</li><li>'디퓨저 섬유 리드스틱 화이트 50개입 디퓨저 섬유 옵션없음 '</li></ul> |
| 11.0 | <ul><li>'아모스 휘핑 컬리 펌 옵션없음 티비'</li><li>'과일나라 컨퓸 씨피 헤어 글레이즈 600ml 3개팩 옵션없음 (주)금옥승유통'</li><li>'[소망] 감자 시스테인 1,2제 각 100ml 옵션없음 희망 미용유통'</li></ul> |
| 5.0 | <ul><li>'라이프홀릭 매너 커버 니플 밴드 100p 3개 one option 옵션없음 지엘디'</li><li>'홈아트 반짝이 빗세트 2P 브러쉬 머리단정 일자빗 옵션없음 제이커머스'</li><li>'가루 파우더 케이스 30g 노세범 땀띠 파우더 소분 공병 (스푼 ) 30g 선데이베리베스트'</li></ul> |
| 3.0 | <ul><li>'몬스터팩토리 샤샤샥 제모 크림 100g 옵션없음 주식회사스피드런'</li><li>'라끄베르 때밀이 바디필링 살국수 때필링 300ml 1개 옵션없음 제이앤더블유'</li><li>'꽃보다잠 코사랑크림 유칼립투스오일밤 비염 옵션없음 더 웰리스'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.7874 |
## 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_bt_test")
# Run inference
preds = model("바이오더마 센시비오 클렌징밀크 250ml 옵션없음 주식회사 하이유로")
```
<!--
### 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 | 3 | 9.3971 | 26 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0.0 | 242 |
| 1.0 | 134 |
| 2.0 | 161 |
| 3.0 | 324 |
| 4.0 | 141 |
| 5.0 | 130 |
| 6.0 | 267 |
| 7.0 | 133 |
| 8.0 | 257 |
| 9.0 | 251 |
| 10.0 | 63 |
| 11.0 | 117 |
| 12.0 | 152 |
### Training Hyperparameters
- batch_size: (512, 512)
- num_epochs: (50, 50)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 60
- 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 |
|:-------:|:-----:|:-------------:|:---------------:|
| 0.0036 | 1 | 0.4112 | - |
| 0.1799 | 50 | 0.3996 | - |
| 0.3597 | 100 | 0.3542 | - |
| 0.5396 | 150 | 0.3073 | - |
| 0.7194 | 200 | 0.2654 | - |
| 0.8993 | 250 | 0.2289 | - |
| 1.0791 | 300 | 0.1949 | - |
| 1.2590 | 350 | 0.1619 | - |
| 1.4388 | 400 | 0.1254 | - |
| 1.6187 | 450 | 0.0899 | - |
| 1.7986 | 500 | 0.0645 | - |
| 1.9784 | 550 | 0.0506 | - |
| 2.1583 | 600 | 0.0403 | - |
| 2.3381 | 650 | 0.0365 | - |
| 2.5180 | 700 | 0.0342 | - |
| 2.6978 | 750 | 0.0329 | - |
| 2.8777 | 800 | 0.0302 | - |
| 3.0576 | 850 | 0.0286 | - |
| 3.2374 | 900 | 0.0272 | - |
| 3.4173 | 950 | 0.0246 | - |
| 3.5971 | 1000 | 0.0229 | - |
| 3.7770 | 1050 | 0.0206 | - |
| 3.9568 | 1100 | 0.0139 | - |
| 4.1367 | 1150 | 0.0083 | - |
| 4.3165 | 1200 | 0.0071 | - |
| 4.4964 | 1250 | 0.0071 | - |
| 4.6763 | 1300 | 0.0057 | - |
| 4.8561 | 1350 | 0.0045 | - |
| 5.0360 | 1400 | 0.0036 | - |
| 5.2158 | 1450 | 0.0031 | - |
| 5.3957 | 1500 | 0.0011 | - |
| 5.5755 | 1550 | 0.0006 | - |
| 5.7554 | 1600 | 0.0004 | - |
| 5.9353 | 1650 | 0.0004 | - |
| 6.1151 | 1700 | 0.0003 | - |
| 6.2950 | 1750 | 0.0003 | - |
| 6.4748 | 1800 | 0.0002 | - |
| 6.6547 | 1850 | 0.0002 | - |
| 6.8345 | 1900 | 0.0002 | - |
| 7.0144 | 1950 | 0.0002 | - |
| 7.1942 | 2000 | 0.0002 | - |
| 7.3741 | 2050 | 0.0002 | - |
| 7.5540 | 2100 | 0.0001 | - |
| 7.7338 | 2150 | 0.0002 | - |
| 7.9137 | 2200 | 0.0001 | - |
| 8.0935 | 2250 | 0.0001 | - |
| 8.2734 | 2300 | 0.0002 | - |
| 8.4532 | 2350 | 0.0002 | - |
| 8.6331 | 2400 | 0.0005 | - |
| 8.8129 | 2450 | 0.0003 | - |
| 8.9928 | 2500 | 0.0002 | - |
| 9.1727 | 2550 | 0.0001 | - |
| 9.3525 | 2600 | 0.0001 | - |
| 9.5324 | 2650 | 0.0002 | - |
| 9.7122 | 2700 | 0.0001 | - |
| 9.8921 | 2750 | 0.0002 | - |
| 10.0719 | 2800 | 0.0001 | - |
| 10.2518 | 2850 | 0.0001 | - |
| 10.4317 | 2900 | 0.0002 | - |
| 10.6115 | 2950 | 0.0003 | - |
| 10.7914 | 3000 | 0.0002 | - |
| 10.9712 | 3050 | 0.0004 | - |
| 11.1511 | 3100 | 0.0003 | - |
| 11.3309 | 3150 | 0.0002 | - |
| 11.5108 | 3200 | 0.0001 | - |
| 11.6906 | 3250 | 0.0001 | - |
| 11.8705 | 3300 | 0.0001 | - |
| 12.0504 | 3350 | 0.0001 | - |
| 12.2302 | 3400 | 0.0 | - |
| 12.4101 | 3450 | 0.0 | - |
| 12.5899 | 3500 | 0.0001 | - |
| 12.7698 | 3550 | 0.0001 | - |
| 12.9496 | 3600 | 0.0003 | - |
| 13.1295 | 3650 | 0.0002 | - |
| 13.3094 | 3700 | 0.0002 | - |
| 13.4892 | 3750 | 0.0004 | - |
| 13.6691 | 3800 | 0.0002 | - |
| 13.8489 | 3850 | 0.0001 | - |
| 14.0288 | 3900 | 0.0001 | - |
| 14.2086 | 3950 | 0.0002 | - |
| 14.3885 | 4000 | 0.0001 | - |
| 14.5683 | 4050 | 0.0001 | - |
| 14.7482 | 4100 | 0.0 | - |
| 14.9281 | 4150 | 0.0001 | - |
| 15.1079 | 4200 | 0.0003 | - |
| 15.2878 | 4250 | 0.0002 | - |
| 15.4676 | 4300 | 0.0001 | - |
| 15.6475 | 4350 | 0.0001 | - |
| 15.8273 | 4400 | 0.0 | - |
| 16.0072 | 4450 | 0.0 | - |
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| 17.2662 | 4800 | 0.0001 | - |
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| 17.8058 | 4950 | 0.0 | - |
| 17.9856 | 5000 | 0.0002 | - |
| 18.1655 | 5050 | 0.0002 | - |
| 18.3453 | 5100 | 0.0002 | - |
| 18.5252 | 5150 | 0.0005 | - |
| 18.7050 | 5200 | 0.0001 | - |
| 18.8849 | 5250 | 0.0 | - |
| 19.0647 | 5300 | 0.0 | - |
| 19.2446 | 5350 | 0.0 | - |
| 19.4245 | 5400 | 0.0 | - |
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| 19.7842 | 5500 | 0.0 | - |
| 19.9640 | 5550 | 0.0001 | - |
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| 20.5036 | 5700 | 0.0002 | - |
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| 21.0432 | 5850 | 0.0003 | - |
| 21.2230 | 5900 | 0.0002 | - |
| 21.4029 | 5950 | 0.0001 | - |
| 21.5827 | 6000 | 0.0 | - |
| 21.7626 | 6050 | 0.0 | - |
| 21.9424 | 6100 | 0.0 | - |
| 22.1223 | 6150 | 0.0 | - |
| 22.3022 | 6200 | 0.0 | - |
| 22.4820 | 6250 | 0.0 | - |
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| 25.0 | 6950 | 0.0 | - |
| 25.1799 | 7000 | 0.0 | - |
| 25.3597 | 7050 | 0.0 | - |
| 25.5396 | 7100 | 0.0 | - |
| 25.7194 | 7150 | 0.0 | - |
| 25.8993 | 7200 | 0.0001 | - |
| 26.0791 | 7250 | 0.0001 | - |
| 26.2590 | 7300 | 0.0005 | - |
| 26.4388 | 7350 | 0.0002 | - |
| 26.6187 | 7400 | 0.0 | - |
| 26.7986 | 7450 | 0.0 | - |
| 26.9784 | 7500 | 0.0 | - |
| 27.1583 | 7550 | 0.0 | - |
| 27.3381 | 7600 | 0.0 | - |
| 27.5180 | 7650 | 0.0 | - |
| 27.6978 | 7700 | 0.0 | - |
| 27.8777 | 7750 | 0.0002 | - |
| 28.0576 | 7800 | 0.0001 | - |
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| 28.4173 | 7900 | 0.0 | - |
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| 28.7770 | 8000 | 0.0001 | - |
| 28.9568 | 8050 | 0.0001 | - |
| 29.1367 | 8100 | 0.0001 | - |
| 29.3165 | 8150 | 0.0001 | - |
| 29.4964 | 8200 | 0.0 | - |
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| 30.0360 | 8350 | 0.0 | - |
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| 30.9353 | 8600 | 0.0 | - |
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| 34.8921 | 9700 | 0.0 | - |
| 35.0719 | 9750 | 0.0 | - |
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| 35.4317 | 9850 | 0.0001 | - |
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| 35.7914 | 9950 | 0.0 | - |
| 35.9712 | 10000 | 0.0 | - |
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| 49.8201 | 13850 | 0.0 | - |
| 50.0 | 13900 | 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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