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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: 리엔 흑모비책 골드 염색약 90g X 3개 (자연갈색/짙은갈색/흑갈색/흑색) 짙은갈색 3개 위메프 > 생활·주방·반려동물 > 바디/헤어
> 샴푸/린스/헤어케어;위메프 > 생활·주방·반려동물 > 세제/구강 > 세탁세제/섬유유연제;위메프 > 생활·주방·반려동물 > 바디/헤어 >
샴푸/린스/헤어케어 > 샴푸/린스;위메프 > 생활·주방·반려동물 > 세제/구강 > 세탁세제/섬유유연제 > 세탁세제;위메프 > 뷰티 > 선케어
> 선밤/선스틱 > 선밤/선스틱;위메프 > 뷰티 > 선케어 > 선크림/선블록 > 선크림/선블록;위메프 > 뷰티 > 바디/헤어 > 헤어염색/파마/왁스
> 염색약;위메프 > 뷰티 > 바디/헤어 > 바디케어/워시/제모 > 바디워시/스크럽;위메프 > 뷰티 > 바디/헤어 > 샴푸/린스/헤어케어 >
샴푸/린스;위메프 > 생활·주방용품 > 세제/구강 > 세탁세제/섬유유연제;위메프 > 생활·주방·반려동물 > 바디/헤어 > 헤어염색/파마/왁스;(#M)위메프
> 생활·주방용품 > 바디/헤어 > 헤어염색/파마/왁스 > 염색약 위메프 > 뷰티 > 바디/헤어 > 샴푸/린스/헤어케어
- text: 로레알 헤어케어 매직 리터치 75ml 새치 빈틈 브라운 LotteOn > 뷰티 > 헤어/바디 > 헤어스타일링 > 헤어스프레이 LotteOn
> 뷰티 > 헤어/바디 > 헤어스타일링 > 헤어스프레이
- text: 로레알파리 매직 리터치 75 ml 브라운 × 1개 LotteOn > 뷰티 > 헤어/바디 > 헤어스타일링 > 헤어스프레이 LotteOn
> 뷰티 > 헤어/바디 > 헤어스타일링 > 헤어스프레이
- text: 아모스 스타일 익스프레션 몰딩 글레이즈300ml MinSellAmount (#M)바디/헤어>헤어스타일링>헤어글레이즈 Gmarket >
뷰티 > 바디/헤어 > 헤어스타일링 > 헤어글레이즈
- text: 시세이도 프리미언스 엔리치 염색약 80g 새치 프리미언스(패션/멋내기)_웜베이지 Wbe-6_(산화제포함) (#M)홈>화장품/미용>헤어스타일링>염색약
Naverstore > 화장품/미용 > 헤어스타일링 > 염색약
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: 0.9464963578536986
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>'이희 마블 에센스 헤어 팩트 다크브라운 본품 LotteOn > 뷰티 > 헤어스타일링 > 염색약 LotteOn > 뷰티 > 헤어스타일링 > 염색약'</li><li>'더마클라센 스타일앤 볼륨짱짱 흑채 스프레이 블랙 120ml x5 MinSellAmount (#M)바디/헤어>헤어스타일링>염색약 Gmarket > 뷰티 > 바디/헤어 > 헤어스타일링 > 염색약'</li><li>'이희 마블 에센스 헤어 팩트 블랙 본품 (#M)홈>화장품/미용>헤어케어>탈모케어 Naverstore > 화장품/미용 > 헤어케어 > 탈모케어'</li></ul> |
| 2 | <ul><li>'웰라 크레아틴 플러스 쉐이프 N 펌 에멀전/건강/파마약 (#M)화장품/미용>헤어스타일링>파마약>웨이브 AD > traverse > Naverstore > 화장품/미용 > 헤어케어 > 파마약 > 웨이브'</li><li>'아모스 루미네이터 익스트림/하드/노멀/소프트/택 MinSellAmount (#M)바디/헤어>헤어스타일링>탈색제 Gmarket > 뷰티 > 바디/헤어 > 헤어스타일링 > 탈색제'</li><li>'아모스 실키블루밍 펌 1제2제 SET 파마약 MinSellAmount (#M)바디/헤어>헤어스타일링>파마약 Gmarket > 뷰티 > 바디/헤어 > 헤어스타일링 > 파마약'</li></ul> |
| 5 | <ul><li>'300ml펌프형 아르드포 헤어젤 (#M)SSG.COM/헤어/바디/헤어스타일링/헤어왁스/젤 ssg > 뷰티 > 헤어/바디 > 헤어스타일링 > 헤어왁스/젤'</li><li>'아르드포 헤어케어 헤어젤 180ml (#M)SSG.COM/헤어/바디/헤어기기/소품/기타헤어기기 ssg > 뷰티 > 헤어/바디 > 헤어기기/소품 > 기타헤어기기'</li><li>'(NC)LG 아르드포 헤어젤 튜브 180ml (#M)SSG.COM/헤어/바디/헤어기기/소품/기타헤어기기 ssg > 뷰티 > 헤어/바디 > 헤어기기/소품 > 기타헤어기기'</li></ul> |
| 0 | <ul><li>'엘라스틴 살롱드컬러 새치염색약 100g x3개 +샴푸 증정 03)밝은갈색 ssg > 뷰티 > 헤어/바디 > 헤어스타일링 > 염색약;ssg > 뷰티 > 미용기기/소품 > 바디관리기기;ssg > 뷰티 > 헤어/바디 > 헤어케어 > 샴푸;ssg > 뷰티 > 헤어/바디 > 헤어케어;ssg > 뷰티 > 헤어/바디 > 헤어스타일링 ssg > 뷰티 > 헤어/바디 > 헤어스타일링'</li><li>'댕기머리 포르테 프레스티지 4종옵션 /한방칼라크림 새치머리 염색약 4호 (자연갈색) (#M)11st>헤어케어>염색약>새치용염색약 11st > 뷰티 > 헤어케어 > 염색약 > 새치용염색약'</li><li>'리엔 흑모비책 골드 염색약 1입 x3개 자연갈색 (#M)바디/헤어>헤어스타일링>염색약 Gmarket > 뷰티 > 바디/헤어 > 헤어스타일링 > 염색약'</li></ul> |
| 4 | <ul><li>'아르드포 헤어스프레이 280ml (#M)SSG.COM/헤어/바디/헤어기기/소품/기타헤어기기 ssg > 뷰티 > 헤어/바디 > 헤어기기/소품 > 기타헤어기기'</li><li>'꽃을든남자 헤어케어시스템 헤어 스프레이(달콤한과일향) 300ml x 5개 MinSellAmount (#M)바디/헤어>헤어스타일링>헤어스프레이 Gmarket > 뷰티 > 바디/헤어 > 헤어스타일링 > 헤어스프레이'</li><li>'헤드스파7 블루밍매직 헤어스타일러 50ml MinSellAmount (#M)바디/헤어>헤어케어>헤어트리트먼트 Gmarket > 뷰티 > 바디/헤어 > 헤어케어 > 헤어트리트먼트'</li></ul> |
| 1 | <ul><li>'미쟝센 컬링에센스2X 숏스타일 150ml x2 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 트리트먼트/헤어팩 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 트리트먼트/헤어팩'</li><li>'미쟝센 컬링에센스2X 숏스타일 230ml 미쟝센 컬링에센스2X 숏스타일 230ml 홈>헤어케어>스타일링/에센스X>헤어에센스X;홈>헤어케어>스타일링/에센스>헤어에센스;(#M)홈>헤어케어>에센스>에센스 OLIVEYOUNG > 헤어케어 > 에센스 > 에센스'</li><li>'4개)미쟝센스테이지컬렉션 컬링에센스2X 탄력웨이브150ml 선택없음 Coupang > 뷰티 > 헤어 > 헤어스타일링 > 컬크림;(#M)쿠팡 홈>뷰티>헤어>헤어스타일링>컬크림 Coupang > 뷰티 > 헤어 > 헤어스타일링 > 컬크림'</li></ul> |
| 3 | <ul><li>'128 브러쉬 단품없음 LotteOn > 뷰티 > 뷰티기기 > 액세서리/소모품 LotteOn > 뷰티 > 뷰티기기 > 액세서리/소모품'</li><li>'리엔 (엘라스틴) 살롱드 컬러 팡팡 헤어쿠션 (짙은갈색) x 3개 짙은갈색 (#M)바디/헤어>헤어스타일링>염색약 Gmarket > 뷰티 > 바디/헤어 > 헤어스타일링 > 염색약'</li><li>'[이지피지] 해피펀치 헤어 커버스틱 3.5g (옵션) 옵션:1호 라이트 헤어 쿠팡 홈>뷰티>뷰티소품>피부관리기>롤러/마사지기;쿠팡 홈>선물스토어>생일선물>여성선물>이미용가전>롤링미용기기;쿠팡 홈>선물스토어>생일>이미용가전>셀프스킨케어>롤링미용기기;(#M)쿠팡 홈>뷰티>헤어>염색/파마>헤어메이크업>헤어섀도/마스카라 Coupang > 뷰티 > 헤어 > 염색/파마 > 헤어메이크업 > 헤어섀도/마스카라'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9465 |
## 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_bt12")
# Run inference
preds = model("아모스 스타일 익스프레션 몰딩 글레이즈300ml MinSellAmount (#M)바디/헤어>헤어스타일링>헤어글레이즈 Gmarket > 뷰티 > 바디/헤어 > 헤어스타일링 > 헤어글레이즈")
```
<!--
### 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 | 11 | 22.4371 | 93 |
| 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 | 0.5286 | - |
| 0.0914 | 50 | 0.4469 | - |
| 0.1828 | 100 | 0.4235 | - |
| 0.2742 | 150 | 0.361 | - |
| 0.3656 | 200 | 0.2736 | - |
| 0.4570 | 250 | 0.1705 | - |
| 0.5484 | 300 | 0.0988 | - |
| 0.6399 | 350 | 0.0709 | - |
| 0.7313 | 400 | 0.0516 | - |
| 0.8227 | 450 | 0.0467 | - |
| 0.9141 | 500 | 0.0477 | - |
| 1.0055 | 550 | 0.0442 | - |
| 1.0969 | 600 | 0.0241 | - |
| 1.1883 | 650 | 0.0238 | - |
| 1.2797 | 700 | 0.0213 | - |
| 1.3711 | 750 | 0.0248 | - |
| 1.4625 | 800 | 0.0202 | - |
| 1.5539 | 850 | 0.0209 | - |
| 1.6453 | 900 | 0.0206 | - |
| 1.7367 | 950 | 0.0203 | - |
| 1.8282 | 1000 | 0.0229 | - |
| 1.9196 | 1050 | 0.011 | - |
| 2.0110 | 1100 | 0.0003 | - |
| 2.1024 | 1150 | 0.0002 | - |
| 2.1938 | 1200 | 0.0002 | - |
| 2.2852 | 1250 | 0.0001 | - |
| 2.3766 | 1300 | 0.0003 | - |
| 2.4680 | 1350 | 0.0001 | - |
| 2.5594 | 1400 | 0.0001 | - |
| 2.6508 | 1450 | 0.0 | - |
| 2.7422 | 1500 | 0.0 | - |
| 2.8336 | 1550 | 0.0 | - |
| 2.9250 | 1600 | 0.0 | - |
| 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 | 0.0004 | - |
| 3.6563 | 2000 | 0.0003 | - |
| 3.7477 | 2050 | 0.0004 | - |
| 3.8391 | 2100 | 0.001 | - |
| 3.9305 | 2150 | 0.0005 | - |
| 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 | 0.0003 | - |
| 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 | 0.0001 | - |
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| 29.9817 | 16400 | 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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