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---
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: 엔프라니 옴므 선블록 썬크림 남성용 선크림 (#M)화장품/미용>남성화장품>선크림 Naverstore > 화장품/미용 > 남성화장품
> 선크림
- text: (시세이도)(시세이도)(특별한정) 파란자차 50ml 세트(+파란자차 정품 용량) NEW 파란자차 (정품) (#M)화장품/향수>선케어>선크림
Gmarket > 뷰티 > 화장품/향수 > 선케어 > 선크림
- text: 에스쁘아 워터스플래쉬 선크림 SPF50+ PA+++ 60ml × 5개 (#M)쿠팡 홈>뷰티>스킨케어>선케어/태닝>선케어>선블록/선크림/선로션
Coupang > 뷰티 > 스킨케어 > 선케어/태닝 > 선케어 > 선블록/선크림/선로션
- text: 이니스프리 인텐시브 롱래스팅 선스크린50ml 50ml × 6개 LotteOn > 뷰티 > 남성화장품 > 스킨 LotteOn > 뷰티
> 남성화장품 > 스킨
- text: 에스트라 리제덤 RX 듀얼 선크림 +BB 50ml 병원전용제품 (#M)SSG.COM/메이크업/베이스메이크업/BB/CC크림 ssg
> 뷰티 > 메이크업 > 베이스메이크업 > BB/CC크림
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: 0.4902962206332993
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>'이니스프리 노세범 선쿠션 SPF50+ PA++++ 14g × 2개 (#M)위메프 > 뷰티 > 메이크업 > 베이스 메이크업 > 파우더/팩트 위메프 > 뷰티 > 메이크업 > 베이스 메이크업 > 파우더/팩트'</li><li>'스킨 세팅 톤업 선 쿠션(리필포함) + 추가구성품 톤업 선 쿠션 LotteOn > 백화점 > 뷰티 > 상단 배너 (Mobile) LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 쿠션/팩트'</li><li>'이니스프리 노세범 선쿠션 리필 14g 1 +1 (#M)쿠팡 홈>뷰티>스킨케어>선케어/태닝>선케어>선스틱 Coupang > 뷰티 > 로드샵 > 스킨케어 > 선케어/태닝'</li></ul> |
| 1 | <ul><li>'SUNDANCE 썬댄스 햇빛 차단+태닝 선스프레이 LSF 50, 200ml ssg > 뷰티 > 스킨케어 > 선케어 > 선스프레이 ssg > 뷰티 > 스킨케어 > 선케어 > 선스프레이'</li><li>'리더스 여름자외선 썬버디 올 오버 선 스프레이 180ml MinSellAmount (#M)화장품/향수>선케어>선스프레이 Gmarket > 뷰티 > 화장품/향수 > 선케어 > 선스프레이'</li><li>'온더바디 헬로키티 에코 썬 스프레이 120ml+120ml 기획세트 (#M)홈>화장품/미용>선케어>선케어세트 Naverstore > 화장품/미용 > 선케어 > 선케어세트'</li></ul> |
| 0 | <ul><li>'[피지오겔] [정가 85,000원] 레드 수딩 AI 에어리 썬스틱 1+1 특별기획 롯데홈쇼핑 > 뷰티 > 남성화장품 LotteOn > 뷰티 > 남성화장품 > 선크림'</li><li>'[빌리프][2106] 해피 보 이지워시 선스틱 18g 세트(타임스퀘어점패션관) (#M)11st>선케어>선밤>선밤 11st > 뷰티 > 선케어 > 선밤 > 선밤'</li><li>'피지오겔 레드 수딩 AI 에어리 썬스틱 7g 1+1(2개) (#M)홈>스킨케어>선케어 HMALL > 뷰티 > 스킨케어 > 선케어'</li></ul> |
| 4 | <ul><li>'오스트레일리안골드 헴프네이션 오리지널 탠 익스텐더 바디로션 535ml (#M)SSG.COM/스킨케어/선케어/태닝 ssg > 뷰티 > 스킨케어 > 선케어 > 태닝'</li><li>'수딩앤모이스처 알로에베라92%수딩젤300ml (#M)홈>화장품/미용>바디케어>바디로션 Naverstore > 화장품/미용 > 바디케어 > 바디로션'</li><li>'세인트 트로페즈 셀프 탠 익스프레스 어드밴스드 브론징 무스 200ml (#M)SSG.COM/스킨케어/선케어/태닝 ssg > 뷰티 > 스킨케어 > 선케어 > 태닝'</li></ul> |
| 3 | <ul><li>'[맥퀸뉴욕] 1+ 1 UV 데일리 모이스처(수분) 선크림 1+1 UV 데일리 모이스처 선크림 (#M)SSG.COM/메이크업/립메이크업/립글로스 ssg > 뷰티 > 메이크업 > 아이메이크업 > 아이라이너'</li><li>'[공식] 더마비 10주년 바디로션/기획세트/멀티오일/프레쉬/크림/워시 1+1 S11.(애브리데이) 대용량 선블록 200ml×2개_S1.튜브견본(랜덤) 쇼킹딜 홈;쇼킹딜 홈>뷰티>바디/향수>바디케어;11st>뷰티>바디/향수>바디케어;11st>바디케어>바디로션>바디로션;11st > 뷰티 > 바디케어 > 바디로션 11st Hour Event > 패션/뷰티 > 뷰티 > 바디/향수 > 바디케어'</li><li>'[20%찜+T11%+묶음+당일 ] 롬앤 11번가 런칭! 모든 취향 취급 중! 밀크 그로서리 외 BEST 1+1 옵션31. 제로 선 클린 단품_01 프레쉬 쇼킹딜 홈>뷰티>선케어/메이크업>립/치크메이크업;11st>메이크업>립메이크업>립틴트;11st>뷰티>선케어/메이크업>립/치크메이크업;11st>뷰티>선케어/메이크업>아이메이크업;11st>메이크업>아이메이크업>마스카라;11st Hour Event > 패션/뷰티 > 뷰티 > 선케어/메이크업 > 립/치크메이크업 11st Hour Event > 패션/뷰티 > 뷰티 > 선케어/메이크업 > 아이메이크업'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.4903 |
## 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_top8_test")
# Run inference
preds = model("엔프라니 옴므 선블록 썬크림 남성용 선크림 (#M)화장품/미용>남성화장품>선크림 Naverstore > 화장품/미용 > 남성화장품 > 선크림")
```
<!--
### 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 | 21.656 | 72 |
| 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 | 0.4513 | - |
| 0.1279 | 50 | 0.4435 | - |
| 0.2558 | 100 | 0.4063 | - |
| 0.3836 | 150 | 0.3413 | - |
| 0.5115 | 200 | 0.2997 | - |
| 0.6394 | 250 | 0.2434 | - |
| 0.7673 | 300 | 0.1724 | - |
| 0.8951 | 350 | 0.1334 | - |
| 1.0230 | 400 | 0.1078 | - |
| 1.1509 | 450 | 0.0997 | - |
| 1.2788 | 500 | 0.0937 | - |
| 1.4066 | 550 | 0.0933 | - |
| 1.5345 | 600 | 0.0909 | - |
| 1.6624 | 650 | 0.0897 | - |
| 1.7903 | 700 | 0.0842 | - |
| 1.9182 | 750 | 0.0741 | - |
| 2.0460 | 800 | 0.0764 | - |
| 2.1739 | 850 | 0.0745 | - |
| 2.3018 | 900 | 0.0733 | - |
| 2.4297 | 950 | 0.0748 | - |
| 2.5575 | 1000 | 0.0718 | - |
| 2.6854 | 1050 | 0.0568 | - |
| 2.8133 | 1100 | 0.0415 | - |
| 2.9412 | 1150 | 0.0256 | - |
| 3.0691 | 1200 | 0.0233 | - |
| 3.1969 | 1250 | 0.0128 | - |
| 3.3248 | 1300 | 0.0088 | - |
| 3.4527 | 1350 | 0.0066 | - |
| 3.5806 | 1400 | 0.0058 | - |
| 3.7084 | 1450 | 0.006 | - |
| 3.8363 | 1500 | 0.0058 | - |
| 3.9642 | 1550 | 0.0039 | - |
| 4.0921 | 1600 | 0.0043 | - |
| 4.2199 | 1650 | 0.0033 | - |
| 4.3478 | 1700 | 0.0059 | - |
| 4.4757 | 1750 | 0.0065 | - |
| 4.6036 | 1800 | 0.0061 | - |
| 4.7315 | 1850 | 0.0052 | - |
| 4.8593 | 1900 | 0.0054 | - |
| 4.9872 | 1950 | 0.0043 | - |
| 5.1151 | 2000 | 0.0064 | - |
| 5.2430 | 2050 | 0.0042 | - |
| 5.3708 | 2100 | 0.0046 | - |
| 5.4987 | 2150 | 0.0038 | - |
| 5.6266 | 2200 | 0.0031 | - |
| 5.7545 | 2250 | 0.0021 | - |
| 5.8824 | 2300 | 0.0006 | - |
| 6.0102 | 2350 | 0.0003 | - |
| 6.1381 | 2400 | 0.0001 | - |
| 6.2660 | 2450 | 0.0002 | - |
| 6.3939 | 2500 | 0.0 | - |
| 6.5217 | 2550 | 0.0 | - |
| 6.6496 | 2600 | 0.0001 | - |
| 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 | - |
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| 8.9514 | 3500 | 0.0 | - |
| 9.0793 | 3550 | 0.0 | - |
| 9.2072 | 3600 | 0.0 | - |
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| 9.5908 | 3750 | 0.0 | - |
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| 9.8465 | 3850 | 0.0 | - |
| 9.9744 | 3900 | 0.0 | - |
| 10.1023 | 3950 | 0.0 | - |
| 10.2302 | 4000 | 0.0 | - |
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| 20.4604 | 8000 | 0.0 | - |
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| 21.3555 | 8350 | 0.0 | - |
| 21.4834 | 8400 | 0.0 | - |
| 21.6113 | 8450 | 0.0 | - |
| 21.7391 | 8500 | 0.0 | - |
| 21.8670 | 8550 | 0.0 | - |
| 21.9949 | 8600 | 0.0 | - |
| 22.1228 | 8650 | 0.0 | - |
| 22.2506 | 8700 | 0.0 | - |
| 22.3785 | 8750 | 0.0 | - |
| 22.5064 | 8800 | 0.0 | - |
| 22.6343 | 8850 | 0.0 | - |
| 22.7621 | 8900 | 0.0 | - |
| 22.8900 | 8950 | 0.0 | - |
| 23.0179 | 9000 | 0.0 | - |
| 23.1458 | 9050 | 0.0 | - |
| 23.2737 | 9100 | 0.0 | - |
| 23.4015 | 9150 | 0.0 | - |
| 23.5294 | 9200 | 0.0 | - |
| 23.6573 | 9250 | 0.0 | - |
| 23.7852 | 9300 | 0.0 | - |
| 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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