---
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           | -               |
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| 29.9636 | 16450 | 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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