---
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: '[라네즈] [체리 블러썸] 워터슬리핑마스크 EX 70ml 상세 설명 참조 (#M)쿠팡 홈>뷰티>스킨케어>마스크/팩>슬리핑팩 Coupang
    > 뷰티 > 스킨케어 > 마스크/팩 > 슬리핑팩'
- text: 메디힐 티트리 케어솔루션 에센셜 마스크 이엑스  LotteOn > 뷰티 > 마스크/팩 > 마스크팩 LotteOn > 뷰티 > 마스크/팩
    > 마스크팩
- text: 이니스프리 블랙티 유스 인핸싱 앰플 마스크 28ml 1개입 × 5개 LotteOn > 뷰티 > 스킨케어 > 마스크/팩 > 마스크팩 LotteOn
    > 뷰티 > 스킨케어 > 마스크/팩 > 마스크팩
- text: 메디힐 마스크팩 티트리 수분 보습 진정 트러블 30. 메디힐 M.E.N 타임톡스_[1장] 홈>메디힐;홈>스킨케어>마스크팩;(#M)홈>화장품/미용>마스크/팩>마스크시트
    Naverstore > 화장품/미용 > 마스크/팩 > 마스크시트
- text: 이니스프리 블랙티 유스 인핸싱 앰플 마스크 28ml 1개입 × 5개 LotteOn > 뷰티 > 스킨케어 > 스킨/토너 LotteOn
    > 뷰티 > 스킨케어 > 스킨/토너
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: 0.5683229813664596
      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:** 4 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 3     | <ul><li>'[묶음할인~25%+T11%]에뛰드 타임어택 ~60% 전품목 빅세일/호랑이의 해 무직타이거 콜라보 런칭 50.패치(1)_매끈반짝3단코팩5개_650000010398 쇼킹딜 홈>뷰티>선케어/메이크업>아이메이크업;11st>메이크업>아이메이크업>아이섀도우;11st>뷰티>선케어/메이크업>아이메이크업;11st Hour Event > 패션/뷰티 > 뷰티 > 선케어/메이크업 > 아이메이크업 11st Hour Event > 패션/뷰티 > 뷰티 > 선케어/메이크업 > 아이메이크업'</li><li>'차앤박 안티포어 블랙헤드 클리어 키트 스트립  (#M)홈>화장품/미용>마스크/팩>코팩 Naverstore > 화장품/미용 > 마스크/팩 > 코팩'</li><li>'[차앤박] CNP 안티포어 블랙헤드 클리어 키트 스트립 3세트(3회분)  (#M)위메프 > 뷰티 > 스킨케어 > 팩/마스크 > 코팩 위메프 > 뷰티 > 스킨케어 > 팩/마스크 > 코팩'</li></ul>                                                                                                                    |
| 0     | <ul><li>'[10%+15%]한스킨 6월 클리어런스 클렌징오일/토너패드/에센스/블랙헤드/마스크~81%OF 블레미쉬 커버 컨실러_브라이트 [GH990355] 쇼킹딜 홈>뷰티>선케어/메이크업>페이스메이크업;11st>뷰티>선케어/메이크업>페이스메이크업;11st>메이크업>페이스메이크업>BB크림;11st > 뷰티 > 메이크업 > 페이스메이크업 11st Hour Event > 패션/뷰티 > 뷰티 > 선케어/메이크업 > 페이스메이크업'</li><li>'네이처리퍼블릭 [네이처리퍼블릭][1+1]수딩 앤 모이스처 알로에베라 수딩젤 마스크시트 단일옵션 × 선택완료 쿠팡 홈>뷰티>스킨케어>마스크/팩>코팩/기타패치>기타패치;Coupang > 뷰티 > 로드샵 > 스킨케어 > 마스크/팩 > 코팩/기타패치 > 기타패치;(#M)쿠팡 홈>뷰티>스킨케어>마스크/팩>패치/코팩>기타패치 Coupang > 뷰티 > 스킨케어 > 마스크/팩 > 패치/코팩 > 기타패치'</li><li>'이니스프리 블랙티 유스 인핸싱 앰플 마스크 28ml 1개입 × 5개 LotteOn > 뷰티 > 스킨케어 > 마스크/팩 > 마스크팩 LotteOn > 뷰티 > 스킨케어 > 마스크/팩 > 마스크팩'</li></ul> |
| 2     | <ul><li>'[쿠폰30%+스토어10%]에뛰드 ~64% 21년 신제품 앵콜전(플레이컬러아이즈/그림자쉐딩/픽싱틴트/순정) 58.AC 클린업_핑크마스크_111080503 쇼킹딜 홈>뷰티>스킨케어>크림;쇼킹딜 홈>뷰티>스킨케어>스킨/로션;11st>스킨케어>스킨/토너>스킨/토너;11st>메이크업>아이메이크업>아이섀도우;쇼킹딜 홈>뷰티>선케어/메이크업>아이메이크업;11st>뷰티>선케어/메이크업>아이메이크업;11st > timedeal 11st Hour Event > 패션/뷰티 > 뷰티 > 선케어/메이크업 > 아이메이크업'</li><li>'마스크 오브 매그너민티 315g 파워 마스크  (#M)뷰티>헤어/바디/미용기기>헤어케어>기획세트 CJmall > 뷰티 > 헤어/바디/미용기기 > 헤어스타일링 > 왁스/스프레이'</li><li>'[말썽피부케어추천] 쑥뜸팩+쑥카밍젤  (#M)위메프 > 뷰티 > 클렌징/필링 > 필링젤/스크럽 > 필링젤/스크럽 위메프 > 뷰티 > 클렌징/필링 > 필링젤/스크럽 > 필링젤/스크럽'</li></ul>                                                                                |
| 1     | <ul><li>'티르티르 물광 콜라겐 生생크림 버블팩 물광마스크 노워시 80ml   당일출고 티르티르콜라겐80ml (#M)홈>화장품/미용>스킨케어>크림 Naverstore > 화장품/미용 > 스킨케어 > 크림'</li><li>"달바 모델 한혜진's pick 화이트트러플 세럼 7통+아이크림1통 단일상품 TV쇼핑>TV쇼핑 화장품/이미용>화장품/향수>기초스킨케어;(#M)TV상품>TV쇼핑 화장품/이미용>화장품/향수>기초스킨케어 CJmall > 뷰티 > 화장품/향수 > 더모코스메틱 > 에센스/세럼/오일"</li><li>'시슬리 벨벳 슬리핑 마스크  LotteOn > 뷰티 > 남성화장품 > 남성화장품세트 LotteOn > 뷰티 > 남성화장품 > 남성화장품세트'</li></ul>                                                                                                                                                                                                                              |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.5683   |

## 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_bt_top3_test")
# Run inference
preds = model("메디힐 티트리 케어솔루션 에센셜 마스크 이엑스  LotteOn > 뷰티 > 마스크/팩 > 마스크팩 LotteOn > 뷰티 > 마스크/팩 > 마스크팩")
```

<!--
### 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  | 22.655 | 91  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 50                    |
| 1     | 50                    |
| 2     | 50                    |
| 3     | 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 |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0032  | 1    | 0.478         | -               |
| 0.1597  | 50   | 0.4392        | -               |
| 0.3195  | 100  | 0.4128        | -               |
| 0.4792  | 150  | 0.3767        | -               |
| 0.6390  | 200  | 0.3406        | -               |
| 0.7987  | 250  | 0.2889        | -               |
| 0.9585  | 300  | 0.2482        | -               |
| 1.1182  | 350  | 0.2336        | -               |
| 1.2780  | 400  | 0.1948        | -               |
| 1.4377  | 450  | 0.1284        | -               |
| 1.5974  | 500  | 0.0958        | -               |
| 1.7572  | 550  | 0.0893        | -               |
| 1.9169  | 600  | 0.0788        | -               |
| 2.0767  | 650  | 0.0706        | -               |
| 2.2364  | 700  | 0.058         | -               |
| 2.3962  | 750  | 0.0476        | -               |
| 2.5559  | 800  | 0.0406        | -               |
| 2.7157  | 850  | 0.0327        | -               |
| 2.8754  | 900  | 0.0198        | -               |
| 3.0351  | 950  | 0.0183        | -               |
| 3.1949  | 1000 | 0.0131        | -               |
| 3.3546  | 1050 | 0.0093        | -               |
| 3.5144  | 1100 | 0.005         | -               |
| 3.6741  | 1150 | 0.0004        | -               |
| 3.8339  | 1200 | 0.0001        | -               |
| 3.9936  | 1250 | 0.0001        | -               |
| 4.1534  | 1300 | 0.0           | -               |
| 4.3131  | 1350 | 0.0001        | -               |
| 4.4728  | 1400 | 0.0           | -               |
| 4.6326  | 1450 | 0.0           | -               |
| 4.7923  | 1500 | 0.0           | -               |
| 4.9521  | 1550 | 0.0           | -               |
| 5.1118  | 1600 | 0.0           | -               |
| 5.2716  | 1650 | 0.0006        | -               |
| 5.4313  | 1700 | 0.0001        | -               |
| 5.5911  | 1750 | 0.0           | -               |
| 5.7508  | 1800 | 0.0           | -               |
| 5.9105  | 1850 | 0.0           | -               |
| 6.0703  | 1900 | 0.0           | -               |
| 6.2300  | 1950 | 0.0           | -               |
| 6.3898  | 2000 | 0.0           | -               |
| 6.5495  | 2050 | 0.0           | -               |
| 6.7093  | 2100 | 0.0           | -               |
| 6.8690  | 2150 | 0.0           | -               |
| 7.0288  | 2200 | 0.0           | -               |
| 7.1885  | 2250 | 0.0           | -               |
| 7.3482  | 2300 | 0.0           | -               |
| 7.5080  | 2350 | 0.0           | -               |
| 7.6677  | 2400 | 0.0           | -               |
| 7.8275  | 2450 | 0.0           | -               |
| 7.9872  | 2500 | 0.0           | -               |
| 8.1470  | 2550 | 0.0           | -               |
| 8.3067  | 2600 | 0.0002        | -               |
| 8.4665  | 2650 | 0.0           | -               |
| 8.6262  | 2700 | 0.0           | -               |
| 8.7859  | 2750 | 0.0001        | -               |
| 8.9457  | 2800 | 0.0           | -               |
| 9.1054  | 2850 | 0.0           | -               |
| 9.2652  | 2900 | 0.0           | -               |
| 9.4249  | 2950 | 0.0002        | -               |
| 9.5847  | 3000 | 0.0096        | -               |
| 9.7444  | 3050 | 0.0007        | -               |
| 9.9042  | 3100 | 0.0006        | -               |
| 10.0639 | 3150 | 0.0005        | -               |
| 10.2236 | 3200 | 0.0001        | -               |
| 10.3834 | 3250 | 0.0018        | -               |
| 10.5431 | 3300 | 0.0003        | -               |
| 10.7029 | 3350 | 0.0003        | -               |
| 10.8626 | 3400 | 0.0           | -               |
| 11.0224 | 3450 | 0.0016        | -               |
| 11.1821 | 3500 | 0.0058        | -               |
| 11.3419 | 3550 | 0.0055        | -               |
| 11.5016 | 3600 | 0.005         | -               |
| 11.6613 | 3650 | 0.0062        | -               |
| 11.8211 | 3700 | 0.0017        | -               |
| 11.9808 | 3750 | 0.0002        | -               |
| 12.1406 | 3800 | 0.0001        | -               |
| 12.3003 | 3850 | 0.0           | -               |
| 12.4601 | 3900 | 0.0           | -               |
| 12.6198 | 3950 | 0.0           | -               |
| 12.7796 | 4000 | 0.0           | -               |
| 12.9393 | 4050 | 0.0           | -               |
| 13.0990 | 4100 | 0.0           | -               |
| 13.2588 | 4150 | 0.0           | -               |
| 13.4185 | 4200 | 0.0           | -               |
| 13.5783 | 4250 | 0.0           | -               |
| 13.7380 | 4300 | 0.0           | -               |
| 13.8978 | 4350 | 0.0           | -               |
| 14.0575 | 4400 | 0.0           | -               |
| 14.2173 | 4450 | 0.0           | -               |
| 14.3770 | 4500 | 0.0           | -               |
| 14.5367 | 4550 | 0.0           | -               |
| 14.6965 | 4600 | 0.0           | -               |
| 14.8562 | 4650 | 0.0           | -               |
| 15.0160 | 4700 | 0.0           | -               |
| 15.1757 | 4750 | 0.0           | -               |
| 15.3355 | 4800 | 0.0           | -               |
| 15.4952 | 4850 | 0.0           | -               |
| 15.6550 | 4900 | 0.0           | -               |
| 15.8147 | 4950 | 0.0           | -               |
| 15.9744 | 5000 | 0.0           | -               |
| 16.1342 | 5050 | 0.0           | -               |
| 16.2939 | 5100 | 0.0           | -               |
| 16.4537 | 5150 | 0.0           | -               |
| 16.6134 | 5200 | 0.0           | -               |
| 16.7732 | 5250 | 0.0           | -               |
| 16.9329 | 5300 | 0.0           | -               |
| 17.0927 | 5350 | 0.0           | -               |
| 17.2524 | 5400 | 0.0           | -               |
| 17.4121 | 5450 | 0.0           | -               |
| 17.5719 | 5500 | 0.0           | -               |
| 17.7316 | 5550 | 0.0           | -               |
| 17.8914 | 5600 | 0.0           | -               |
| 18.0511 | 5650 | 0.0           | -               |
| 18.2109 | 5700 | 0.0           | -               |
| 18.3706 | 5750 | 0.0           | -               |
| 18.5304 | 5800 | 0.0           | -               |
| 18.6901 | 5850 | 0.0           | -               |
| 18.8498 | 5900 | 0.0           | -               |
| 19.0096 | 5950 | 0.0           | -               |
| 19.1693 | 6000 | 0.0           | -               |
| 19.3291 | 6050 | 0.0           | -               |
| 19.4888 | 6100 | 0.0           | -               |
| 19.6486 | 6150 | 0.0           | -               |
| 19.8083 | 6200 | 0.0           | -               |
| 19.9681 | 6250 | 0.0           | -               |
| 20.1278 | 6300 | 0.0           | -               |
| 20.2875 | 6350 | 0.0           | -               |
| 20.4473 | 6400 | 0.0           | -               |
| 20.6070 | 6450 | 0.0           | -               |
| 20.7668 | 6500 | 0.0           | -               |
| 20.9265 | 6550 | 0.0           | -               |
| 21.0863 | 6600 | 0.0           | -               |
| 21.2460 | 6650 | 0.0           | -               |
| 21.4058 | 6700 | 0.0           | -               |
| 21.5655 | 6750 | 0.0           | -               |
| 21.7252 | 6800 | 0.0           | -               |
| 21.8850 | 6850 | 0.0           | -               |
| 22.0447 | 6900 | 0.0           | -               |
| 22.2045 | 6950 | 0.0           | -               |
| 22.3642 | 7000 | 0.0           | -               |
| 22.5240 | 7050 | 0.0           | -               |
| 22.6837 | 7100 | 0.0           | -               |
| 22.8435 | 7150 | 0.0           | -               |
| 23.0032 | 7200 | 0.0           | -               |
| 23.1629 | 7250 | 0.0           | -               |
| 23.3227 | 7300 | 0.0           | -               |
| 23.4824 | 7350 | 0.0           | -               |
| 23.6422 | 7400 | 0.0           | -               |
| 23.8019 | 7450 | 0.0           | -               |
| 23.9617 | 7500 | 0.0           | -               |
| 24.1214 | 7550 | 0.0           | -               |
| 24.2812 | 7600 | 0.0           | -               |
| 24.4409 | 7650 | 0.0           | -               |
| 24.6006 | 7700 | 0.0           | -               |
| 24.7604 | 7750 | 0.0           | -               |
| 24.9201 | 7800 | 0.0           | -               |
| 25.0799 | 7850 | 0.0           | -               |
| 25.2396 | 7900 | 0.0           | -               |
| 25.3994 | 7950 | 0.0           | -               |
| 25.5591 | 8000 | 0.0           | -               |
| 25.7188 | 8050 | 0.0           | -               |
| 25.8786 | 8100 | 0.0           | -               |
| 26.0383 | 8150 | 0.0           | -               |
| 26.1981 | 8200 | 0.0           | -               |
| 26.3578 | 8250 | 0.0           | -               |
| 26.5176 | 8300 | 0.0           | -               |
| 26.6773 | 8350 | 0.0           | -               |
| 26.8371 | 8400 | 0.0           | -               |
| 26.9968 | 8450 | 0.0           | -               |
| 27.1565 | 8500 | 0.0           | -               |
| 27.3163 | 8550 | 0.0           | -               |
| 27.4760 | 8600 | 0.0           | -               |
| 27.6358 | 8650 | 0.0           | -               |
| 27.7955 | 8700 | 0.0           | -               |
| 27.9553 | 8750 | 0.0           | -               |
| 28.1150 | 8800 | 0.0           | -               |
| 28.2748 | 8850 | 0.0           | -               |
| 28.4345 | 8900 | 0.0           | -               |
| 28.5942 | 8950 | 0.0           | -               |
| 28.7540 | 9000 | 0.0           | -               |
| 28.9137 | 9050 | 0.0           | -               |
| 29.0735 | 9100 | 0.0           | -               |
| 29.2332 | 9150 | 0.0           | -               |
| 29.3930 | 9200 | 0.0           | -               |
| 29.5527 | 9250 | 0.0           | -               |
| 29.7125 | 9300 | 0.0           | -               |
| 29.8722 | 9350 | 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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