setfit safetensors roberta sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:klue/roberta-base base_model:finetune:klue/roberta-base model-index region:us
---
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 클래식 래쉬 컬러 스튜디오 컬렉션 160543 (#M)SSG.COM/메이크업/베이스메이크업/메이크업베이스 ssg > 뷰티 >
    메이크업 > 베이스메이크업 > 메이크업베이스
- text: 더툴랩 더스타일래쉬 4종리얼/내츄럴/볼륨/맥스 중 택1 003 볼륨 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품 >
    브로우관리 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품 > 브로우관리
- text: Tweezerman 클래식 아이래쉬 컬러 속눈썹 뷰러  (#M)화장품/미용>뷰티소품>아이소품>눈썹칼 Naverstore > 화장품/미용
    > 뷰티소품 > 아이소품 > 눈썹칼
- text: 에뛰드하우스 [에뛰드 추가쿠폰] 마이뷰티툴 속눈썹 3호 캣 아이 (#M)GSSHOP>뷰티>베이스메이크업>메이크업베이스 GSSHOP >
    뷰티 > 베이스메이크업 > 메이크업베이스
- text: 미샤 시크릿 래쉬 - 1호 디어  (#M)홈>화장품/미용>뷰티소품>아이소품>속눈썹/속눈썹펌제 Naverstore > 화장품/미용 >
    뷰티소품 > 아이소품 > 속눈썹/속눈썹펌제
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: 0.9622489959839358
      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:** 5 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                                                                                                                                                                                                                                                                                                                                                                          |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 5     | <ul><li>'[에뛰드] 마이뷰티툴 속눈썹 1ea 5호 홈>화장소품;홈>TOOL;(#M)홈>배송비 절약템 🛒 Naverstore > 화장품/미용 > 뷰티소품 > 아이소품 > 속눈썹/속눈썹펌제'</li><li>'더툴랩 더 스타일 래쉬 볼륨 TSL003 블랙 × 45개 (#M)쿠팡 홈>뷰티>뷰티소품>아이소품>속눈썹관리 소품 Coupang > 뷰티 > 뷰티소품 > 아이소품 > 속눈썹관리 소품'</li><li>'더툴랩 해피림 아이래쉬 내추럴 가닥속눈썹 1pack 11.5N (#M)화장품/미용>뷰티소품>아이소품>속눈썹/속눈썹펌제 Naverstore > 화장품/미용 > 뷰티소품 > 아이소품 > 속눈썹/속눈썹펌제'</li></ul>                   |
| 1     | <ul><li>'트위저맨 Tweezerman 스테인리스 브로우 쉐이핑 가위 및 브러시 521626  (#M)홈>화장품/미용>뷰티소품>헤어소품>미용가위 Naverstore > 화장품/미용 > 뷰티소품 > 헤어소품 > 미용가위'</li><li>'트위저맨 스테인리스 브로우 셰이핑 시져 브러쉬 70238 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 베이스/프라이머 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 베이스/프라이머'</li><li>'트위저맨 스테인리스 브로우 셰이핑 시져 브러쉬 70238 ssg > 뷰티 > 메이크업 > 베이스메이크업 > 파운데이션 ssg > 뷰티 > 메이크업 > 베이스메이크업 > 파운데이션'</li></ul> |
| 0     | <ul><li>'피카소 속눈썹 빗 피카소 속눈썹 빗 (#M)홈>미용소품>기타소품>기타미용소품 OLIVEYOUNG > 미용소품 > 기타소품 > 기타미용소품'</li><li>'트위저맨 프로페셔널 폴딩 아이래쉬콤브 1개 (#M)쿠팡 홈>뷰티>뷰티소품>페이스소품>브러쉬 Coupang > 뷰티 > 뷰티소품 > 페이스소품 > 브러쉬'</li><li>'속눈썹롯드 속눈썹펌롯드 L컬(SHARP) (#M)홈>화장품/미용>뷰티소품>아이소품>기타아이소품 Naverstore > 화장품/미용 > 뷰티소품 > 아이소품 > 기타아이소품'</li></ul>                                                                         |
| 3     | <ul><li>'[Tweezerman] 트위저맨 클래식 속눈썹 뷰러 로즈 골드 1개입  (#M)홈>화장품/미용>뷰티소품>메이크업브러시>브러시세트 Naverstore > 화장품/미용 > 뷰티소품 > 메이크업브러시 > 브러시세트'</li><li>'[에뛰드] 마이 뷰티툴 뷰러 (1EA)  (#M)홈>TOOL Naverstore > 화장품/미용 > 뷰티소품 > 아이소품 > 뷰러'</li><li>'시세이도 213뷰러 전체뷰러 속눈썹 고데기+뷰러리필  LotteOn > 뷰티 > 뷰티소품 > 아이소품 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품 > 속눈썹관리'</li></ul>                                          |
| 4     | <ul><li>'e.l.f. 듀얼 펜슬 샤프너 10세트 (#M)쿠팡 홈>뷰티>뷰티소품>아이소품>족집게/샤프너 Coupang > 뷰티 > 뷰티소품 > 아이소품 > 족집게/샤프너'</li><li>'e.l.f. 듀얼 펜슬 샤프너 혼합 색상 × 6개입 (#M)쿠팡 홈>뷰티>뷰티소품>아이소품>족집게/샤프너 Coupang > 뷰티 > 뷰티소품 > 아이소품 > 족집게/샤프너'</li><li>'[맥]펜슬 샤프너  (#M)홈>화장품/미용>뷰티소품>아이소품>샤프너 Naverstore > 화장품/미용 > 뷰티소품 > 아이소품 > 샤프너'</li></ul>                                                                    |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9622   |

## 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_cate_top_bt6_3")
# Run inference
preds = model("Tweezerman 클래식 아이래쉬 컬러 속눈썹 뷰러  (#M)화장품/미용>뷰티소품>아이소품>눈썹칼 Naverstore > 화장품/미용 > 뷰티소품 > 아이소품 > 눈썹칼")
```

<!--
### 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   | 13  | 20.7017 | 46  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 50                    |
| 1     | 9                     |
| 3     | 50                    |
| 4     | 22                    |
| 5     | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0035  | 1    | 0.4788        | -               |
| 0.1767  | 50   | 0.4758        | -               |
| 0.3534  | 100  | 0.4629        | -               |
| 0.5300  | 150  | 0.3827        | -               |
| 0.7067  | 200  | 0.2023        | -               |
| 0.8834  | 250  | 0.0454        | -               |
| 1.0601  | 300  | 0.0031        | -               |
| 1.2367  | 350  | 0.0001        | -               |
| 1.4134  | 400  | 0.0001        | -               |
| 1.5901  | 450  | 0.0           | -               |
| 1.7668  | 500  | 0.0           | -               |
| 1.9435  | 550  | 0.0001        | -               |
| 2.1201  | 600  | 0.0           | -               |
| 2.2968  | 650  | 0.0           | -               |
| 2.4735  | 700  | 0.0           | -               |
| 2.6502  | 750  | 0.0           | -               |
| 2.8269  | 800  | 0.0           | -               |
| 3.0035  | 850  | 0.0           | -               |
| 3.1802  | 900  | 0.0           | -               |
| 3.3569  | 950  | 0.0           | -               |
| 3.5336  | 1000 | 0.0           | -               |
| 3.7102  | 1050 | 0.0           | -               |
| 3.8869  | 1100 | 0.0           | -               |
| 4.0636  | 1150 | 0.0           | -               |
| 4.2403  | 1200 | 0.0           | -               |
| 4.4170  | 1250 | 0.0001        | -               |
| 4.5936  | 1300 | 0.0012        | -               |
| 4.7703  | 1350 | 0.0006        | -               |
| 4.9470  | 1400 | 0.0           | -               |
| 5.1237  | 1450 | 0.0           | -               |
| 5.3004  | 1500 | 0.0           | -               |
| 5.4770  | 1550 | 0.0           | -               |
| 5.6537  | 1600 | 0.0           | -               |
| 5.8304  | 1650 | 0.0           | -               |
| 6.0071  | 1700 | 0.0           | -               |
| 6.1837  | 1750 | 0.0           | -               |
| 6.3604  | 1800 | 0.0           | -               |
| 6.5371  | 1850 | 0.0           | -               |
| 6.7138  | 1900 | 0.0           | -               |
| 6.8905  | 1950 | 0.0           | -               |
| 7.0671  | 2000 | 0.0           | -               |
| 7.2438  | 2050 | 0.0           | -               |
| 7.4205  | 2100 | 0.0           | -               |
| 7.5972  | 2150 | 0.0           | -               |
| 7.7739  | 2200 | 0.0           | -               |
| 7.9505  | 2250 | 0.0           | -               |
| 8.1272  | 2300 | 0.0           | -               |
| 8.3039  | 2350 | 0.0           | -               |
| 8.4806  | 2400 | 0.0           | -               |
| 8.6572  | 2450 | 0.0           | -               |
| 8.8339  | 2500 | 0.0           | -               |
| 9.0106  | 2550 | 0.0           | -               |
| 9.1873  | 2600 | 0.0           | -               |
| 9.3640  | 2650 | 0.0           | -               |
| 9.5406  | 2700 | 0.0           | -               |
| 9.7173  | 2750 | 0.0           | -               |
| 9.8940  | 2800 | 0.0           | -               |
| 10.0707 | 2850 | 0.0           | -               |
| 10.2473 | 2900 | 0.0           | -               |
| 10.4240 | 2950 | 0.0           | -               |
| 10.6007 | 3000 | 0.0           | -               |
| 10.7774 | 3050 | 0.0           | -               |
| 10.9541 | 3100 | 0.0           | -               |
| 11.1307 | 3150 | 0.0           | -               |
| 11.3074 | 3200 | 0.0           | -               |
| 11.4841 | 3250 | 0.0           | -               |
| 11.6608 | 3300 | 0.0           | -               |
| 11.8375 | 3350 | 0.0           | -               |
| 12.0141 | 3400 | 0.0           | -               |
| 12.1908 | 3450 | 0.0           | -               |
| 12.3675 | 3500 | 0.0           | -               |
| 12.5442 | 3550 | 0.0           | -               |
| 12.7208 | 3600 | 0.0           | -               |
| 12.8975 | 3650 | 0.0           | -               |
| 13.0742 | 3700 | 0.0           | -               |
| 13.2509 | 3750 | 0.0           | -               |
| 13.4276 | 3800 | 0.0           | -               |
| 13.6042 | 3850 | 0.0           | -               |
| 13.7809 | 3900 | 0.0           | -               |
| 13.9576 | 3950 | 0.0           | -               |
| 14.1343 | 4000 | 0.0           | -               |
| 14.3110 | 4050 | 0.0           | -               |
| 14.4876 | 4100 | 0.0           | -               |
| 14.6643 | 4150 | 0.0           | -               |
| 14.8410 | 4200 | 0.0           | -               |
| 15.0177 | 4250 | 0.0           | -               |
| 15.1943 | 4300 | 0.0           | -               |
| 15.3710 | 4350 | 0.0           | -               |
| 15.5477 | 4400 | 0.0           | -               |
| 15.7244 | 4450 | 0.0           | -               |
| 15.9011 | 4500 | 0.0           | -               |
| 16.0777 | 4550 | 0.0           | -               |
| 16.2544 | 4600 | 0.0           | -               |
| 16.4311 | 4650 | 0.0           | -               |
| 16.6078 | 4700 | 0.0           | -               |
| 16.7845 | 4750 | 0.0           | -               |
| 16.9611 | 4800 | 0.0           | -               |
| 17.1378 | 4850 | 0.0           | -               |
| 17.3145 | 4900 | 0.0           | -               |
| 17.4912 | 4950 | 0.0           | -               |
| 17.6678 | 5000 | 0.0           | -               |
| 17.8445 | 5050 | 0.0           | -               |
| 18.0212 | 5100 | 0.0           | -               |
| 18.1979 | 5150 | 0.0           | -               |
| 18.3746 | 5200 | 0.0           | -               |
| 18.5512 | 5250 | 0.0           | -               |
| 18.7279 | 5300 | 0.0           | -               |
| 18.9046 | 5350 | 0.0           | -               |
| 19.0813 | 5400 | 0.0           | -               |
| 19.2580 | 5450 | 0.0           | -               |
| 19.4346 | 5500 | 0.0           | -               |
| 19.6113 | 5550 | 0.0           | -               |
| 19.7880 | 5600 | 0.0           | -               |
| 19.9647 | 5650 | 0.0           | -               |
| 20.1413 | 5700 | 0.0           | -               |
| 20.3180 | 5750 | 0.0           | -               |
| 20.4947 | 5800 | 0.0           | -               |
| 20.6714 | 5850 | 0.0           | -               |
| 20.8481 | 5900 | 0.0           | -               |
| 21.0247 | 5950 | 0.0           | -               |
| 21.2014 | 6000 | 0.0           | -               |
| 21.3781 | 6050 | 0.0           | -               |
| 21.5548 | 6100 | 0.0           | -               |
| 21.7314 | 6150 | 0.0           | -               |
| 21.9081 | 6200 | 0.0           | -               |
| 22.0848 | 6250 | 0.0           | -               |
| 22.2615 | 6300 | 0.0           | -               |
| 22.4382 | 6350 | 0.0           | -               |
| 22.6148 | 6400 | 0.0           | -               |
| 22.7915 | 6450 | 0.0           | -               |
| 22.9682 | 6500 | 0.0           | -               |
| 23.1449 | 6550 | 0.0           | -               |
| 23.3216 | 6600 | 0.0           | -               |
| 23.4982 | 6650 | 0.0           | -               |
| 23.6749 | 6700 | 0.0           | -               |
| 23.8516 | 6750 | 0.0           | -               |
| 24.0283 | 6800 | 0.0           | -               |
| 24.2049 | 6850 | 0.0           | -               |
| 24.3816 | 6900 | 0.0           | -               |
| 24.5583 | 6950 | 0.0           | -               |
| 24.7350 | 7000 | 0.0           | -               |
| 24.9117 | 7050 | 0.0           | -               |
| 25.0883 | 7100 | 0.0           | -               |
| 25.2650 | 7150 | 0.0           | -               |
| 25.4417 | 7200 | 0.0           | -               |
| 25.6184 | 7250 | 0.0           | -               |
| 25.7951 | 7300 | 0.0           | -               |
| 25.9717 | 7350 | 0.0           | -               |
| 26.1484 | 7400 | 0.0           | -               |
| 26.3251 | 7450 | 0.0           | -               |
| 26.5018 | 7500 | 0.0           | -               |
| 26.6784 | 7550 | 0.0           | -               |
| 26.8551 | 7600 | 0.0           | -               |
| 27.0318 | 7650 | 0.0           | -               |
| 27.2085 | 7700 | 0.0           | -               |
| 27.3852 | 7750 | 0.0           | -               |
| 27.5618 | 7800 | 0.0           | -               |
| 27.7385 | 7850 | 0.0           | -               |
| 27.9152 | 7900 | 0.0           | -               |
| 28.0919 | 7950 | 0.0           | -               |
| 28.2686 | 8000 | 0.0           | -               |
| 28.4452 | 8050 | 0.0           | -               |
| 28.6219 | 8100 | 0.0           | -               |
| 28.7986 | 8150 | 0.0           | -               |
| 28.9753 | 8200 | 0.0           | -               |
| 29.1519 | 8250 | 0.0           | -               |
| 29.3286 | 8300 | 0.0           | -               |
| 29.5053 | 8350 | 0.0           | -               |
| 29.6820 | 8400 | 0.0           | -               |
| 29.8587 | 8450 | 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->