setfit safetensors roberta sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:mini1013/master_domain base_model:finetune:mini1013/master_domain model-index region:us
---
base_model: mini1013/master_domain
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: '[라 메르]루미너스 리프팅 쿠션 파운데이션 SPF 20 [00003] 로즈 아이보리 (#M)11st>메이크업>페이스메이크업>파운데이션
    11st > 뷰티 > 메이크업 > 페이스메이크업 > 파운데이션'
- text: 바비브라운 인텐시브 스킨 세럼 파운데이션 30ml (SPF40) 샌드 (#M)화장품/미용>베이스메이크업>파운데이션>리퀴드형 Naverstore
    > 화장품/미용 > 베이스메이크업 > 파운데이션 > 리퀴드형
- text: 입생로랑 뚜쉬 에끌라 글로우-팩트 쿠션 B20  LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 쿠션/팩트 LotteOn >
    뷰티 > 메이크업 > 베이스메이크업 > 쿠션/팩트
- text: IOPE 퍼펙트 커버 베이스 35ml 메이크업 컨실러 비비 1호 라이트그린 (#M)SSG.COM/메이크업/베이스메이크업/메이크업베이스
    ssg > 뷰티 > 메이크업 > 베이스메이크업 > 메이크업베이스
- text: 에스쁘아 프로 테일러 파운데이션 비글로우 SPF25 PA++ 1호 포슬린 × 1개 (#M)SSG.COM/메이크업/베이스메이크업/쿠션파운데이션
    ssg > 뷰티 > 메이크업 > 베이스메이크업 > 쿠션파운데이션
inference: true
model-index:
- name: SetFit with mini1013/master_domain
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.9587109768378651
      name: Accuracy
---

# SetFit with mini1013/master_domain

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) 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:** [mini1013/master_domain](https://huggingface.co/mini1013/master_domain)
- **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                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 2     | <ul><li>'랑콤 뗑 이돌 울트라 웨어 스틱 SF 15 No. 04 Beige N 216699 LotteOn > 뷰티 > 베이스메이크업 > 파우더 LotteOn > 뷰티 > 베이스메이크업 > 파우더'</li><li>'조성아 슈퍼핏 메가프루프 스틱파데 1+1+에센스 미스트 100mlx1 21호 (#M)위메프 > 뷰티 > 메이크업 > 베이스 메이크업 > 파운데이션 위메프 > 뷰티 > 메이크업 > 베이스 메이크업 > 파운데이션'</li><li>'에스쁘아 가을 메이크업 빅세일 ~50% / 신상 스틱 파운데이션, 밋츠 그레이 립스틱, 룩북 팔레트 등 총 출동 비글로우스틱파데(아이보리)_611123517 ssg > 뷰티 > 메이크업 > 아이메이크업 > 아이섀도우 ssg > 뷰티 > 메이크업 > 아이메이크업 > 아이섀도우'</li></ul>                                                  |
| 1     | <ul><li>'[백화점정품/당일출고] 맥 스트롭 크림 50ml/핑크라이트(오리지널)  (#M)홈>화장품/미용>베이스메이크업>메이크업베이스 Naverstore > 화장품/미용 > 베이스메이크업 > 메이크업베이스'</li><li>'에브리데이 IOPE 퍼펙트 커버 베이스35ml 메이크업 컨실러 비비 13579EA ◎＆쿠팡 본상품선택 × ◎＆쿠팡 2호 라이트퍼플 (#M)쿠팡 홈>뷰티>메이크업>베이스 메이크업>베이스 메이크업 세트 Coupang > 뷰티 > 메이크업 > 베이스 메이크업 > 베이스 메이크업 세트'</li><li>'맥 스트롭 크림 50ml 실버라이트 (#M)위메프 > 뷰티 > 남성화장품 > 남성 메이크업 > 남성 베이스메이크업 위메프 > 뷰티 > 남성화장품 > 남성 메이크업 > 남성 베이스메이크업'</li></ul>                                                        |
| 0     | <ul><li>'[본사직영][기획]NEW 헤라 실키 스테이 파운데이션+파운데이션 브러쉬(48000원상당 본품동일사양)+블러셔 21W1 (#M)홈>메이크업>베이스메이크업 HMALL > 뷰티 > 메이크업 > 베이스메이크업'</li><li>'CHANEL 레 베쥬 뚜쉬 드 뗑 BR12 (#M)홈>화장품/미용>베이스메이크업>파운데이션>리퀴드형 Naverstore > 화장품/미용 > 베이스메이크업 > 파운데이션 > 리퀴드형'</li><li>'에스티로더 [단독] 더블웨어 파운데이션 세트 (+마스카라 정품 ) 226792 1C0 쉘 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 파운데이션 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 파운데이션'</li></ul>                                                                                     |
| 4     | <ul><li>'에스티 로더 더블 웨어 spf 10 - 36 샌드 30ml  LotteOn > 뷰티 > 베이스메이크업 > 파운데이션 LotteOn > 뷰티 > 베이스메이크업 > 파운데이션'</li><li>'Maybelline New York Maybelline New York Dream Smooth Mousse Foundation, Pure Beige, 0.49 Ounce  LotteOn > 뷰티 > 색조메이크업 > 색조메이크업세트 LotteOn > 뷰티 > 색조메이크업 > 색조메이크업세트'</li><li>'수블리마지 르 뗑 10 베쥬 LotteOn > 뷰티 > 베이스메이크업 > 파운데이션 LotteOn > 뷰티 > 베이스메이크업 > 파운데이션'</li></ul>                                                                                         |
| 3     | <ul><li>'[포렌코즈] 필 워터 쿠션 상세 설명 참조_피부타입:23호 내추럴베이지 (#M)쿠팡 홈>뷰티>메이크업>베이스 메이크업>파운데이션 Coupang > 뷰티 > 메이크업 > 베이스 메이크업 > 파운데이션'</li><li>'[현대백화점][시세이도]UV 프로텍티브 컴팩트 파운데이션 본품 SPF35/PA+++ [00004] 미디엄 아이보리 홈>화장품/미용>베이스메이크업>파운데이션>쿠션형;홈>화장품/미용>선케어>선파우더/쿠션;(#M)홈>화장품/미용>베이스메이크업>파운데이션>리퀴드형 Naverstore > 화장품/미용 > 선케어 > 선파우더/쿠션'</li><li>'롬앤 제로쿠션 spf20pa++ 0g발림성 세미매트쿠션 내추럴21호본품 화장품/향수>에어쿠션/팩트>에어쿠션;(#M)화장품/향수>베이스메이크업>파우더/트윈케이크 Gmarket > 뷰티 > 화장품/향수 > 베이스메이크업 > 파우더/트윈케이크'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9587   |

## 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_bt5_4_test_flat")
# Run inference
preds = model("입생로랑 뚜쉬 에끌라 글로우-팩트 쿠션 B20  LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 쿠션/팩트 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 쿠션/팩트")
```

<!--
### 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   | 10  | 23.036 | 53  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 50                    |
| 1     | 50                    |
| 2     | 50                    |
| 3     | 50                    |
| 4     | 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 |
|:-------:|:-----:|:-------------:|:---------------:|
| 0.0026  | 1     | 0.3977        | -               |
| 0.1279  | 50    | 0.4192        | -               |
| 0.2558  | 100   | 0.3842        | -               |
| 0.3836  | 150   | 0.3421        | -               |
| 0.5115  | 200   | 0.3111        | -               |
| 0.6394  | 250   | 0.2705        | -               |
| 0.7673  | 300   | 0.2216        | -               |
| 0.8951  | 350   | 0.1494        | -               |
| 1.0230  | 400   | 0.0875        | -               |
| 1.1509  | 450   | 0.0521        | -               |
| 1.2788  | 500   | 0.0321        | -               |
| 1.4066  | 550   | 0.004         | -               |
| 1.5345  | 600   | 0.0019        | -               |
| 1.6624  | 650   | 0.0011        | -               |
| 1.7903  | 700   | 0.0005        | -               |
| 1.9182  | 750   | 0.0004        | -               |
| 2.0460  | 800   | 0.0004        | -               |
| 2.1739  | 850   | 0.0003        | -               |
| 2.3018  | 900   | 0.0003        | -               |
| 2.4297  | 950   | 0.0003        | -               |
| 2.5575  | 1000  | 0.0001        | -               |
| 2.6854  | 1050  | 0.0003        | -               |
| 2.8133  | 1100  | 0.0005        | -               |
| 2.9412  | 1150  | 0.0003        | -               |
| 3.0691  | 1200  | 0.0001        | -               |
| 3.1969  | 1250  | 0.0001        | -               |
| 3.3248  | 1300  | 0.0           | -               |
| 3.4527  | 1350  | 0.0001        | -               |
| 3.5806  | 1400  | 0.0           | -               |
| 3.7084  | 1450  | 0.0           | -               |
| 3.8363  | 1500  | 0.0           | -               |
| 3.9642  | 1550  | 0.0           | -               |
| 4.0921  | 1600  | 0.0           | -               |
| 4.2199  | 1650  | 0.0           | -               |
| 4.3478  | 1700  | 0.0           | -               |
| 4.4757  | 1750  | 0.0           | -               |
| 4.6036  | 1800  | 0.0           | -               |
| 4.7315  | 1850  | 0.0           | -               |
| 4.8593  | 1900  | 0.0           | -               |
| 4.9872  | 1950  | 0.0002        | -               |
| 5.1151  | 2000  | 0.0018        | -               |
| 5.2430  | 2050  | 0.0009        | -               |
| 5.3708  | 2100  | 0.0003        | -               |
| 5.4987  | 2150  | 0.0           | -               |
| 5.6266  | 2200  | 0.0002        | -               |
| 5.7545  | 2250  | 0.0           | -               |
| 5.8824  | 2300  | 0.0002        | -               |
| 6.0102  | 2350  | 0.0           | -               |
| 6.1381  | 2400  | 0.0007        | -               |
| 6.2660  | 2450  | 0.0           | -               |
| 6.3939  | 2500  | 0.0           | -               |
| 6.5217  | 2550  | 0.0           | -               |
| 6.6496  | 2600  | 0.0           | -               |
| 6.7775  | 2650  | 0.0           | -               |
| 6.9054  | 2700  | 0.0           | -               |
| 7.0332  | 2750  | 0.0           | -               |
| 7.1611  | 2800  | 0.0           | -               |
| 7.2890  | 2850  | 0.0           | -               |
| 7.4169  | 2900  | 0.0           | -               |
| 7.5448  | 2950  | 0.0           | -               |
| 7.6726  | 3000  | 0.0           | -               |
| 7.8005  | 3050  | 0.0           | -               |
| 7.9284  | 3100  | 0.0           | -               |
| 8.0563  | 3150  | 0.0           | -               |
| 8.1841  | 3200  | 0.0           | -               |
| 8.3120  | 3250  | 0.0           | -               |
| 8.4399  | 3300  | 0.0           | -               |
| 8.5678  | 3350  | 0.0           | -               |
| 8.6957  | 3400  | 0.0           | -               |
| 8.8235  | 3450  | 0.0           | -               |
| 8.9514  | 3500  | 0.0           | -               |
| 9.0793  | 3550  | 0.0           | -               |
| 9.2072  | 3600  | 0.0           | -               |
| 9.3350  | 3650  | 0.0           | -               |
| 9.4629  | 3700  | 0.0           | -               |
| 9.5908  | 3750  | 0.0           | -               |
| 9.7187  | 3800  | 0.0           | -               |
| 9.8465  | 3850  | 0.0           | -               |
| 9.9744  | 3900  | 0.0029        | -               |
| 10.1023 | 3950  | 0.0026        | -               |
| 10.2302 | 4000  | 0.0037        | -               |
| 10.3581 | 4050  | 0.0001        | -               |
| 10.4859 | 4100  | 0.0           | -               |
| 10.6138 | 4150  | 0.0003        | -               |
| 10.7417 | 4200  | 0.0           | -               |
| 10.8696 | 4250  | 0.0           | -               |
| 10.9974 | 4300  | 0.0           | -               |
| 11.1253 | 4350  | 0.0           | -               |
| 11.2532 | 4400  | 0.0           | -               |
| 11.3811 | 4450  | 0.0           | -               |
| 11.5090 | 4500  | 0.0           | -               |
| 11.6368 | 4550  | 0.0           | -               |
| 11.7647 | 4600  | 0.0           | -               |
| 11.8926 | 4650  | 0.0           | -               |
| 12.0205 | 4700  | 0.0           | -               |
| 12.1483 | 4750  | 0.0           | -               |
| 12.2762 | 4800  | 0.0           | -               |
| 12.4041 | 4850  | 0.0           | -               |
| 12.5320 | 4900  | 0.0           | -               |
| 12.6598 | 4950  | 0.0           | -               |
| 12.7877 | 5000  | 0.0           | -               |
| 12.9156 | 5050  | 0.0           | -               |
| 13.0435 | 5100  | 0.0           | -               |
| 13.1714 | 5150  | 0.0           | -               |
| 13.2992 | 5200  | 0.0           | -               |
| 13.4271 | 5250  | 0.0           | -               |
| 13.5550 | 5300  | 0.0           | -               |
| 13.6829 | 5350  | 0.0           | -               |
| 13.8107 | 5400  | 0.0           | -               |
| 13.9386 | 5450  | 0.0           | -               |
| 14.0665 | 5500  | 0.0           | -               |
| 14.1944 | 5550  | 0.0           | -               |
| 14.3223 | 5600  | 0.0           | -               |
| 14.4501 | 5650  | 0.0           | -               |
| 14.5780 | 5700  | 0.0           | -               |
| 14.7059 | 5750  | 0.0           | -               |
| 14.8338 | 5800  | 0.0           | -               |
| 14.9616 | 5850  | 0.0           | -               |
| 15.0895 | 5900  | 0.0           | -               |
| 15.2174 | 5950  | 0.0           | -               |
| 15.3453 | 6000  | 0.0           | -               |
| 15.4731 | 6050  | 0.0           | -               |
| 15.6010 | 6100  | 0.0           | -               |
| 15.7289 | 6150  | 0.0           | -               |
| 15.8568 | 6200  | 0.0           | -               |
| 15.9847 | 6250  | 0.0           | -               |
| 16.1125 | 6300  | 0.0           | -               |
| 16.2404 | 6350  | 0.0           | -               |
| 16.3683 | 6400  | 0.0           | -               |
| 16.4962 | 6450  | 0.0           | -               |
| 16.6240 | 6500  | 0.0           | -               |
| 16.7519 | 6550  | 0.0           | -               |
| 16.8798 | 6600  | 0.0           | -               |
| 17.0077 | 6650  | 0.0           | -               |
| 17.1355 | 6700  | 0.0           | -               |
| 17.2634 | 6750  | 0.0           | -               |
| 17.3913 | 6800  | 0.0           | -               |
| 17.5192 | 6850  | 0.0           | -               |
| 17.6471 | 6900  | 0.0           | -               |
| 17.7749 | 6950  | 0.0           | -               |
| 17.9028 | 7000  | 0.0           | -               |
| 18.0307 | 7050  | 0.0           | -               |
| 18.1586 | 7100  | 0.0           | -               |
| 18.2864 | 7150  | 0.0           | -               |
| 18.4143 | 7200  | 0.0           | -               |
| 18.5422 | 7250  | 0.0           | -               |
| 18.6701 | 7300  | 0.0           | -               |
| 18.7980 | 7350  | 0.0           | -               |
| 18.9258 | 7400  | 0.0           | -               |
| 19.0537 | 7450  | 0.0           | -               |
| 19.1816 | 7500  | 0.0           | -               |
| 19.3095 | 7550  | 0.0           | -               |
| 19.4373 | 7600  | 0.0           | -               |
| 19.5652 | 7650  | 0.0           | -               |
| 19.6931 | 7700  | 0.0           | -               |
| 19.8210 | 7750  | 0.0           | -               |
| 19.9488 | 7800  | 0.0           | -               |
| 20.0767 | 7850  | 0.0           | -               |
| 20.2046 | 7900  | 0.0           | -               |
| 20.3325 | 7950  | 0.0           | -               |
| 20.4604 | 8000  | 0.0           | -               |
| 20.5882 | 8050  | 0.0           | -               |
| 20.7161 | 8100  | 0.0           | -               |
| 20.8440 | 8150  | 0.0           | -               |
| 20.9719 | 8200  | 0.0           | -               |
| 21.0997 | 8250  | 0.0           | -               |
| 21.2276 | 8300  | 0.0           | -               |
| 21.3555 | 8350  | 0.0           | -               |
| 21.4834 | 8400  | 0.0           | -               |
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| 21.7391 | 8500  | 0.0           | -               |
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| 22.8900 | 8950  | 0.0           | -               |
| 23.0179 | 9000  | 0.0           | -               |
| 23.1458 | 9050  | 0.0           | -               |
| 23.2737 | 9100  | 0.0           | -               |
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| 23.5294 | 9200  | 0.0           | -               |
| 23.6573 | 9250  | 0.0           | -               |
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| 23.9130 | 9350  | 0.0           | -               |
| 24.0409 | 9400  | 0.0           | -               |
| 24.1688 | 9450  | 0.0           | -               |
| 24.2967 | 9500  | 0.0           | -               |
| 24.4246 | 9550  | 0.0           | -               |
| 24.5524 | 9600  | 0.0           | -               |
| 24.6803 | 9650  | 0.0           | -               |
| 24.8082 | 9700  | 0.0           | -               |
| 24.9361 | 9750  | 0.0           | -               |
| 25.0639 | 9800  | 0.0           | -               |
| 25.1918 | 9850  | 0.0           | -               |
| 25.3197 | 9900  | 0.0           | -               |
| 25.4476 | 9950  | 0.0           | -               |
| 25.5754 | 10000 | 0.0           | -               |
| 25.7033 | 10050 | 0.0           | -               |
| 25.8312 | 10100 | 0.0           | -               |
| 25.9591 | 10150 | 0.0           | -               |
| 26.0870 | 10200 | 0.0           | -               |
| 26.2148 | 10250 | 0.0           | -               |
| 26.3427 | 10300 | 0.0           | -               |
| 26.4706 | 10350 | 0.0           | -               |
| 26.5985 | 10400 | 0.0           | -               |
| 26.7263 | 10450 | 0.0           | -               |
| 26.8542 | 10500 | 0.0           | -               |
| 26.9821 | 10550 | 0.0           | -               |
| 27.1100 | 10600 | 0.0           | -               |
| 27.2379 | 10650 | 0.0           | -               |
| 27.3657 | 10700 | 0.0           | -               |
| 27.4936 | 10750 | 0.0           | -               |
| 27.6215 | 10800 | 0.0           | -               |
| 27.7494 | 10850 | 0.0           | -               |
| 27.8772 | 10900 | 0.0           | -               |
| 28.0051 | 10950 | 0.0           | -               |
| 28.1330 | 11000 | 0.0           | -               |
| 28.2609 | 11050 | 0.0           | -               |
| 28.3887 | 11100 | 0.0           | -               |
| 28.5166 | 11150 | 0.0           | -               |
| 28.6445 | 11200 | 0.0           | -               |
| 28.7724 | 11250 | 0.0           | -               |
| 28.9003 | 11300 | 0.0           | -               |
| 29.0281 | 11350 | 0.0           | -               |
| 29.1560 | 11400 | 0.0           | -               |
| 29.2839 | 11450 | 0.0           | -               |
| 29.4118 | 11500 | 0.0           | -               |
| 29.5396 | 11550 | 0.0           | -               |
| 29.6675 | 11600 | 0.0           | -               |
| 29.7954 | 11650 | 0.0           | -               |
| 29.9233 | 11700 | 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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