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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: 설화수 퍼펙팅 쿠션 에어셀 퍼프 6매 설화수 에어셀 퍼프 6매 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 화장품파우치/정리함
LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 화장품파우치/정리함
- text: Tweezerman 홀리그래픽 마이크로 미니 족집게 세트 (4284-R) Winter Frost (#M)홈>화장품/미용>뷰티소품>페이스소품>기타페이스소품
Naverstore > 화장품/미용 > 뷰티소품 > 페이스소품 > 기타페이스소품
- text: 타투 스티커 현아 마스크 꾸미기 데코 판박이 1장상사맨 3타투스티커-스마일 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품
> 헤나/타투 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 헤나/타투
- text: 비레디 페이스 피팅 브러쉬 포 히어로즈 MinSellAmount (#M)화장품/향수>남성화장품>남성메이크업/BB Gmarket > 뷰티
> 화장품/향수 > 남성화장품 > 남성메이크업/BB
- text: 더툴랩 믹싱 아크릴 팔레트 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 화장품파우치/정리함 LotteOn > 뷰티
> 뷰티기기/소품 > 메이크업소품 > 화장품파우치/정리함
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: 0.736949846468782
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:** 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>'모델링팩 제조 셀프 피부관리 용품 세트 스파츌러 할로윈분장 미용기구 분홍색 (#M)쿠팡 홈>뷰티>메이크업>베이스 메이크업>베이스 메이크업 세트 Coupang > 뷰티 > 메이크업 > 베이스 메이크업 > 베이스 메이크업 세트'</li><li>'조단앤쥬디 플랫 탑 배큐엄 로션 보틀 펌핑용기 TR012 Blue 30ml × 1개 (#M)쿠팡 홈>뷰티>뷰티소품>용기/거울/기타소품>화장품용기 Coupang > 뷰티 > 뷰티소품 > 용기/거울/기타소품 > 화장품용기'</li><li>'프레스식 클렌징 리무버 토너 공병 150ml 혼합색상 × 5개 (#M)쿠팡 홈>뷰티>뷰티소품>용기/거울/기타소품>화장품용기 Coupang > 뷰티 > 뷰티소품 > 용기/거울/기타소품 > 화장품용기'</li></ul> |
| 3 | <ul><li>'아리따움 아이돌 래쉬 프리미엄 22호러블리아이 (#M)홈>화장품/미용>뷰티소품>아이소품>속눈썹/속눈썹펌제 Naverstore > 화장품/미용 > 뷰티소품 > 아이소품 > 속눈썹/속눈썹펌제'</li><li>'시세이도 아이래쉬 213 전체 뷰러 시세이도 뷰러 214 고무리필 x 3개 홈>💡 신상품;홈>전체상품;(#M)홈>💡신상품 Naverstore > 화장품/미용 > 뷰티소품 > 아이소품 > 뷰러'</li><li>'슈에무라 뷰러 아이래쉬컬러 N 전체뷰러 (#M)화장품/미용>뷰티소품>아이소품>뷰러 Naverstore > 화장품/미용 > 뷰티소품 > 아이소품 > 뷰러'</li></ul> |
| 6 | <ul><li>'프리미엄 샴푸 브러쉬 1입_P085124958 옵션/라보에이치 프리미엄 샴푸 브러쉬 1입 ssg > 뷰티 > 헤어/바디 > 헤어스타일링 > 헤어메이크업 ssg > 뷰티 > 헤어/바디 > 헤어스타일링 > 헤어메이크업'</li><li>'모로칸오일 세라믹 볼륨 헤어 드라이 브러쉬 롤빗 5종 모로칸오일브러쉬 45mm LotteOn > 뷰티 > 뷰티소품 > 헤어소품 LotteOn > 뷰티 > 뷰티기기/소품 > 헤어소품 > 빗/헤어브러쉬'</li><li>'필리밀리 포니 훅 헤어세트 리본_시크핑크데님블루 포니 훅 세트(리본_시크핑크) (#M)쿠팡 홈>뷰티>메이크업>립 메이크업>립메이크업세트 Coupang > 뷰티 > 메이크업 > 립 메이크업 > 립메이크업세트'</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>'메디플라워 메이크 셀프 패드 리필 130매x2박스(총260매) 화장솜 각질패드 닥토패드 (#M)11st>뷰티소품>화장솜>화장솜 11st > 뷰티 > 뷰티소품 > 화장솜'</li><li>'라네즈 네오 쿠션 매트or글로우 퍼프 6개 매트 퍼프 (#M)홈>화장품/미용>뷰티소품>페이스소품>퍼프 Naverstore > 화장품/미용 > 뷰티소품 > 페이스소품 > 퍼프'</li><li>'벨로즈 MTS 롤러 더마 페이스 헤어 두피 얼굴 마사지 홈케어 스테인레스 일반형 0.2mm 티타늄_한달패키지(EGF10ppm+롤러2개+에탄올)_0.3mm 홈>화장품/미용>뷰티소품>페이스소품>마사지도구;홈>MTS 도구;홈>전체상품;(#M)홈>MTS Naverstore > 화장품/미용 > 뷰티소품 > 페이스소품 > 마사지도구'</li></ul> |
| 1 | <ul><li>'투쿨포스쿨 아트클래스 비건 멀티 컨투어 브러쉬 비건 멀티 컨투어 브러쉬 LotteOn > 뷰티 > 메이크업 > 쉐딩/컨투어링 LotteOn > 뷰티 > 메이크업 > 쉐딩/컨투어링'</li><li>'그림자쉐딩 02 코 브러쉬 (#M)뷰티>화장품/향수>미용소품>퍼프/스폰지/브러쉬 CJmall > 뷰티 > 화장품/향수 > 선케어 > 선크림/선로션'</li><li>'정샘물 마스터클래스 아이섀도우 L 브러쉬+물크림 라이트 마스크 3매 마스터클래스 아이섀도우 L 브러쉬 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 브러쉬 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 브러쉬'</li></ul> |
| 2 | <ul><li>'에뛰드 마이뷰티툴 효녀손 바디브러쉬 LotteOn > 뷰티 > 뷰티소품 > 페이스소품 > 브러쉬 LotteOn > 뷰티 > 뷰티소품 > 액세서리/소모품/기타'</li><li>'웰라 SP 1000ml 샴푸 전용 펌프 (색상랜덤) (#M)화장품/미용>헤어케어>샴푸 AD > traverse > Naverstore > 화장품/미용 > 헤어케어 > 샴푸 > 비듬샴푸'</li><li>'필리밀리 바디브러시 2종 선인장모 바디브러시 (스트롱) (#M)홈>미용소품>기타소품>클렌징준비도구 OLIVEYOUNG > 미용소품 > 기타소품 > 전체'</li></ul> |
| 4 | <ul><li>'5초눈썹타투스티커5초11쌍 눈썹문신스티커 눈썹타투 눈썹 E11 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 브러쉬 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 브러쉬'</li><li>'태틀리 타투 스티커 유칼립투스 씨네레아 × 2개 LotteOn > 뷰티 > 뷰티기기/소품 > 바디소품 LotteOn > 뷰티 > 뷰티기기/소품 > 바디소품'</li><li>'wjx니들 타투니들 카트리지 엔코 타투용품 반영구 smp 재료 라운드매그넘_1023 (#M)홈>전체상품 Naverstore > 화장품/미용 > 뷰티소품 > 타투'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.7369 |
## 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_bt5_test_flat_top_cate")
# Run inference
preds = model("비레디 페이스 피팅 브러쉬 포 히어로즈 MinSellAmount (#M)화장품/향수>남성화장품>남성메이크업/BB Gmarket > 뷰티 > 화장품/향수 > 남성화장품 > 남성메이크업/BB")
```
<!--
### 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 | 20.6963 | 66 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 1 |
| 1 | 50 |
| 2 | 48 |
| 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 | 0.4261 | - |
| 0.0916 | 50 | 0.4493 | - |
| 0.1832 | 100 | 0.4428 | - |
| 0.2747 | 150 | 0.4252 | - |
| 0.3663 | 200 | 0.3948 | - |
| 0.4579 | 250 | 0.361 | - |
| 0.5495 | 300 | 0.3209 | - |
| 0.6410 | 350 | 0.2692 | - |
| 0.7326 | 400 | 0.2629 | - |
| 0.8242 | 450 | 0.2437 | - |
| 0.9158 | 500 | 0.2383 | - |
| 1.0073 | 550 | 0.2352 | - |
| 1.0989 | 600 | 0.2306 | - |
| 1.1905 | 650 | 0.2165 | - |
| 1.2821 | 700 | 0.2081 | - |
| 1.3736 | 750 | 0.1861 | - |
| 1.4652 | 800 | 0.1676 | - |
| 1.5568 | 850 | 0.1363 | - |
| 1.6484 | 900 | 0.112 | - |
| 1.7399 | 950 | 0.1005 | - |
| 1.8315 | 1000 | 0.0779 | - |
| 1.9231 | 1050 | 0.0613 | - |
| 2.0147 | 1100 | 0.0392 | - |
| 2.1062 | 1150 | 0.0267 | - |
| 2.1978 | 1200 | 0.0213 | - |
| 2.2894 | 1250 | 0.0189 | - |
| 2.3810 | 1300 | 0.0174 | - |
| 2.4725 | 1350 | 0.0135 | - |
| 2.5641 | 1400 | 0.015 | - |
| 2.6557 | 1450 | 0.0108 | - |
| 2.7473 | 1500 | 0.0074 | - |
| 2.8388 | 1550 | 0.0072 | - |
| 2.9304 | 1600 | 0.0073 | - |
| 3.0220 | 1650 | 0.0058 | - |
| 3.1136 | 1700 | 0.0045 | - |
| 3.2051 | 1750 | 0.006 | - |
| 3.2967 | 1800 | 0.0056 | - |
| 3.3883 | 1850 | 0.0039 | - |
| 3.4799 | 1900 | 0.0041 | - |
| 3.5714 | 1950 | 0.0033 | - |
| 3.6630 | 2000 | 0.0045 | - |
| 3.7546 | 2050 | 0.0053 | - |
| 3.8462 | 2100 | 0.0075 | - |
| 3.9377 | 2150 | 0.0017 | - |
| 4.0293 | 2200 | 0.0008 | - |
| 4.1209 | 2250 | 0.0005 | - |
| 4.2125 | 2300 | 0.0007 | - |
| 4.3040 | 2350 | 0.0007 | - |
| 4.3956 | 2400 | 0.0003 | - |
| 4.4872 | 2450 | 0.0013 | - |
| 4.5788 | 2500 | 0.0008 | - |
| 4.6703 | 2550 | 0.0002 | - |
| 4.7619 | 2600 | 0.0 | - |
| 4.8535 | 2650 | 0.0004 | - |
| 4.9451 | 2700 | 0.0001 | - |
| 5.0366 | 2750 | 0.0007 | - |
| 5.1282 | 2800 | 0.0003 | - |
| 5.2198 | 2850 | 0.0003 | - |
| 5.3114 | 2900 | 0.0007 | - |
| 5.4029 | 2950 | 0.0002 | - |
| 5.4945 | 3000 | 0.0012 | - |
| 5.5861 | 3050 | 0.0007 | - |
| 5.6777 | 3100 | 0.0002 | - |
| 5.7692 | 3150 | 0.0007 | - |
| 5.8608 | 3200 | 0.0003 | - |
| 5.9524 | 3250 | 0.0003 | - |
| 6.0440 | 3300 | 0.0003 | - |
| 6.1355 | 3350 | 0.0003 | - |
| 6.2271 | 3400 | 0.0002 | - |
| 6.3187 | 3450 | 0.0005 | - |
| 6.4103 | 3500 | 0.0002 | - |
| 6.5018 | 3550 | 0.0006 | - |
| 6.5934 | 3600 | 0.0005 | - |
| 6.6850 | 3650 | 0.0003 | - |
| 6.7766 | 3700 | 0.0003 | - |
| 6.8681 | 3750 | 0.0009 | - |
| 6.9597 | 3800 | 0.0006 | - |
| 7.0513 | 3850 | 0.0002 | - |
| 7.1429 | 3900 | 0.0005 | - |
| 7.2344 | 3950 | 0.0005 | - |
| 7.3260 | 4000 | 0.0005 | - |
| 7.4176 | 4050 | 0.0005 | - |
| 7.5092 | 4100 | 0.0005 | - |
| 7.6007 | 4150 | 0.0008 | - |
| 7.6923 | 4200 | 0.0009 | - |
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| 7.8755 | 4300 | 0.0 | - |
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| 11.1722 | 6100 | 0.0057 | - |
| 11.2637 | 6150 | 0.004 | - |
| 11.3553 | 6200 | 0.0037 | - |
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| 29.9451 | 16350 | 0.0005 | - |
### 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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