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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: 에뛰드하우스 실키 퍼프 화장솜 80개입 × 1개 (#M)쿠팡 홈>뷰티>뷰티소품>클렌징소품>화장솜/면봉 Coupang > 뷰티 > 뷰티소품
> 화장솜/면봉
- text: 트위저맨 슬랜트 트위저 족집게 로즈골드 × 1개 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 퍼프 LotteOn >
뷰티 > 뷰티기기/소품 > 메이크업소품 > 퍼프
- text: 타투스티커 바디형 문신스티커 헤나 레터링 흉터커버 쇄골 반팔 J type 타투스티커 30종세트 LotteOn > 뷰티 > 뷰티기기/소품
> 메이크업소품 > 헤나/타투 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 헤나/타투
- text: 더툴랩 215 피니쉬 컨실러 파운데이션 브러쉬 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 브러쉬 LotteOn >
뷰티 > 뷰티기기/소품 > 메이크업소품 > 브러쉬
- text: 에뛰드하우스 실키 퍼프 화장솜 80개입 × 1개 (#M)쿠팡 홈>뷰티>뷰티소품>클렌징소품>화장솜/면봉 Coupang > 뷰티 > 뷰티소품
> 화장솜/면봉
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: 0.8526100307062436
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>'프린시아 공용기 로션통 30g 옵션없음 ssg > 뷰티 > 미용기기/소품 > 거울/용기/기타소품 ssg > 뷰티 > 미용기기/소품 > 아이소품 > 인조속눈썹'</li><li>'[텐바이텐] 입생로랑 유광 레드 프레스티지 파우치 옵션선택_옵션선택 (#M)쿠팡 홈>뷰티>남성화장품>남성 쉐이빙 케어>애프터쉐이브 스킨/로션/크림 Coupang > 뷰티 > 남성화장품 > 남성 쉐이빙 케어 > 애프터쉐이브 스킨/로션/크림'</li></ul> |
| 3 | <ul><li>'토니모리 아이래쉬 컬러_동수원점_동수원점 아이래쉬 컬러 (#M)SSG.COM/메이크업/아이메이크업/아이섀도우/글리터/팔레트 ssg > 뷰티 > 메이크업 > 아이메이크업'</li><li>'아리따움 아이돌 래쉬 프리미엄 9호리얼핏 (#M)홈>화장품/미용>뷰티소품>아이소품>속눈썹/속눈썹펌제 Naverstore > 화장품/미용 > 뷰티소품 > 아이소품 > 속눈썹/속눈썹펌제'</li><li>'트위저맨 쁘띠 트위즈 족집게 세트 실버 × 1세트 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 화장품파우치/정리함 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 화장품파우치/정리함'</li></ul> |
| 6 | <ul><li>'토니모리 뿌리 볼륨 헤어 집게 (#M)쿠팡 홈>뷰티>헤어>헤어스타일링>헤어왁스 Coupang > 뷰티 > 로드샵 > 헤어 > 헤어스타일링 > 헤어왁스'</li><li>'갤리포니아 미니 갤리포니아 미니 ssg > 뷰티 > 메이크업 > 치크메이크업;ssg > 뷰티 > 메이크업 > 립메이크업 > 립밤 ssg > 뷰티 > 메이크업 > 립메이크업'</li><li>'보다나 두피케어 샴푸 브러쉬 보다나 두피케어 샴푸 브러쉬 홈>미용소품>헤어소품>헤어브러시;(#M)홈>헤어케어>헤어브러쉬>두피용 OLIVEYOUNG > 미용소품 > 헤어/바디 > 헤어브러시'</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>'헤라 블랙 쿠션 제로 비티 핏 퍼프 2입 파워풀한 핏팅력 균일한 밀착 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 퍼프 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 퍼프'</li><li>'[티타늄] 더마 MTS 롤러 헤어 두피 540 0.25mm 0.5mm 니들 앰플 티타늄_고급형_0.75mm 홈>화장품/미용>뷰티소품>페이스소품>마사지도구;홈>전체상품;(#M)홈>MTS 도구 Naverstore > 화장품/미용 > 뷰티소품 > 페이스소품 > 마사지도구'</li><li>'헤라 블랙 쿠션 퍼프 x 10개/설화수 쿠션 퍼프 x 10개 헤라블랙쿠션퍼프 x 5개 (#M)홈>화장품/미용>뷰티소품>페이스소품>퍼프 Naverstore > 화장품/미용 > 뷰티소품 > 페이스소품 > 퍼프'</li></ul> |
| 1 | <ul><li>'[단품구매] 해피림 아이블랜딩 세트 5종 (10%할인) 235 펜슬 브러쉬 (#M)화장품/미용>뷰티소품>메이크업브러시>아이브러시 Naverstore > 화장품/미용 > 뷰티소품 > 메이크업브러시 > 아이브러시'</li><li>'[안씨브러쉬] 여행용 블러셔, 파우더 브러쉬 - VELVET04 (맑은발색) 홈>라인별>(new) VELVET (Premium);(#M)홈>라인별>VELVET (Premium) Naverstore > 화장품/미용 > 뷰티소품 > 메이크업브러시 > 페이스브러시'</li><li>'[안씨브러쉬] 스몰 아이섀도, 블렌딩 아이섀도 브러쉬 - Eve316 (#M)홈>용도별>아이메이크업>스몰 - 메인컬러, 중간컬러 Naverstore > 화장품/미용 > 뷰티소품 > 메이크업브러시 > 아이브러시'</li></ul> |
| 2 | <ul><li>'바버샵 커트보 미용실 가운 컷트보 드라이보 파마보 염색보 넥셔터 03. 바버샵 그린 스트라이프 홈>전체상품;(#M)홈>커트보,앞치마 Naverstore > 화장품/미용 > 뷰티소품 > 헤어소품 > 기타헤어소품'</li><li>'_ [!BEST_쿠_팡_픽!] _ 롱바디 브러시 각도 조절 가능 등브러쉬 목욕 용품샤워타올목욕용품 _ 5F9AD0 _ 00000EA_goldspo_on mall ★수저픽★ 베이지_JW (#M)쿠팡 홈>생활용품>헤어/바디/세안>샤워/입욕용품>입욕제>바스밤 Coupang > 뷰티 > 바디 > 샤워/입욕용품 > 입욕제'</li><li>'롱바디 브러시 각도 조절 가능 등브러쉬 목욕 용품 SQ+6242EA 밀키 블루 (#M)쿠팡 홈>생활용품>헤어/바디/세안>샤워/입욕용품>입욕제>바스밤 Coupang > 뷰티 > 바디 > 샤워/입욕용품 > 입욕제'</li></ul> |
| 4 | <ul><li>'타투바늘 DiRK 더크 카트리지 니들 라이너, 매그넘, 쉐더 반영구 라운드 매그넘_1211 (#M)홈>화장품/미용>뷰티소품>타투 Naverstore > 화장품/미용 > 뷰티소품 > 타투'</li><li>'[스킨알엑스] [타투미] 브레이슬릿 Chandelier Bracelet LotteOn > 뷰티 > 바디케어 > 바디케어세트 LotteOn > 뷰티 > 바디케어 > 바디케어세트'</li><li>'5초눈썹타투스티커5초11쌍 눈썹문신스티커 눈썹타투 눈썹 E14 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 브러쉬 LotteOn > 뷰티 > 뷰티기기/소품 > 메이크업소품 > 브러쉬'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.8526 |
## 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_top6_test")
# Run inference
preds = model("에뛰드하우스 실키 퍼프 화장솜 80개입 × 1개 (#M)쿠팡 홈>뷰티>뷰티소품>클렌징소품>화장솜/면봉 Coupang > 뷰티 > 뷰티소품 > 화장솜/면봉")
```
<!--
### 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 | 20.66 | 66 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 1 |
| 1 | 50 |
| 2 | 50 |
| 3 | 50 |
| 4 | 50 |
| 5 | 50 |
| 6 | 49 |
| 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 | 0.4049 | - |
| 0.0914 | 50 | 0.4426 | - |
| 0.1828 | 100 | 0.4367 | - |
| 0.2742 | 150 | 0.4123 | - |
| 0.3656 | 200 | 0.3927 | - |
| 0.4570 | 250 | 0.3631 | - |
| 0.5484 | 300 | 0.3095 | - |
| 0.6399 | 350 | 0.2743 | - |
| 0.7313 | 400 | 0.2444 | - |
| 0.8227 | 450 | 0.2342 | - |
| 0.9141 | 500 | 0.2188 | - |
| 1.0055 | 550 | 0.2089 | - |
| 1.0969 | 600 | 0.1942 | - |
| 1.1883 | 650 | 0.1751 | - |
| 1.2797 | 700 | 0.1564 | - |
| 1.3711 | 750 | 0.1525 | - |
| 1.4625 | 800 | 0.1342 | - |
| 1.5539 | 850 | 0.1252 | - |
| 1.6453 | 900 | 0.1124 | - |
| 1.7367 | 950 | 0.1022 | - |
| 1.8282 | 1000 | 0.0877 | - |
| 1.9196 | 1050 | 0.0611 | - |
| 2.0110 | 1100 | 0.0447 | - |
| 2.1024 | 1150 | 0.0353 | - |
| 2.1938 | 1200 | 0.0305 | - |
| 2.2852 | 1250 | 0.0321 | - |
| 2.3766 | 1300 | 0.0299 | - |
| 2.4680 | 1350 | 0.0292 | - |
| 2.5594 | 1400 | 0.0307 | - |
| 2.6508 | 1450 | 0.0328 | - |
| 2.7422 | 1500 | 0.0277 | - |
| 2.8336 | 1550 | 0.0226 | - |
| 2.9250 | 1600 | 0.0103 | - |
| 3.0165 | 1650 | 0.007 | - |
| 3.1079 | 1700 | 0.0024 | - |
| 3.1993 | 1750 | 0.0012 | - |
| 3.2907 | 1800 | 0.0012 | - |
| 3.3821 | 1850 | 0.0007 | - |
| 3.4735 | 1900 | 0.0007 | - |
| 3.5649 | 1950 | 0.0003 | - |
| 3.6563 | 2000 | 0.0006 | - |
| 3.7477 | 2050 | 0.0009 | - |
| 3.8391 | 2100 | 0.0005 | - |
| 3.9305 | 2150 | 0.0005 | - |
| 4.0219 | 2200 | 0.001 | - |
| 4.1133 | 2250 | 0.0044 | - |
| 4.2048 | 2300 | 0.004 | - |
| 4.2962 | 2350 | 0.0042 | - |
| 4.3876 | 2400 | 0.0053 | - |
| 4.4790 | 2450 | 0.0061 | - |
| 4.5704 | 2500 | 0.008 | - |
| 4.6618 | 2550 | 0.0057 | - |
| 4.7532 | 2600 | 0.0063 | - |
| 4.8446 | 2650 | 0.0064 | - |
| 4.9360 | 2700 | 0.0056 | - |
| 5.0274 | 2750 | 0.0033 | - |
| 5.1188 | 2800 | 0.0017 | - |
| 5.2102 | 2850 | 0.0018 | - |
| 5.3016 | 2900 | 0.0012 | - |
| 5.3931 | 2950 | 0.0007 | - |
| 5.4845 | 3000 | 0.0026 | - |
| 5.5759 | 3050 | 0.0038 | - |
| 5.6673 | 3100 | 0.0019 | - |
| 5.7587 | 3150 | 0.0009 | - |
| 5.8501 | 3200 | 0.0005 | - |
| 5.9415 | 3250 | 0.0002 | - |
| 6.0329 | 3300 | 0.0002 | - |
| 6.1243 | 3350 | 0.001 | - |
| 6.2157 | 3400 | 0.0003 | - |
| 6.3071 | 3450 | 0.001 | - |
| 6.3985 | 3500 | 0.0003 | - |
| 6.4899 | 3550 | 0.0008 | - |
| 6.5814 | 3600 | 0.0 | - |
| 6.6728 | 3650 | 0.0006 | - |
| 6.7642 | 3700 | 0.0005 | - |
| 6.8556 | 3750 | 0.0003 | - |
| 6.9470 | 3800 | 0.0004 | - |
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| 12.8885 | 7050 | 0.0038 | - |
| 12.9799 | 7100 | 0.0029 | - |
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| 13.4369 | 7350 | 0.0018 | - |
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| 13.6197 | 7450 | 0.0007 | - |
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| 29.9817 | 16400 | 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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