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	---
	base_model: klue/roberta-base
	library_name: setfit
	metrics:
	- accuracy
	pipeline_tag: text-classification
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: 트위저맨 클래식 래쉬 컬러 스튜디오 컬렉션 160543 (#M)SSG.COM/메이크업/베이스메이크업/메이크업베이스 ssg > 뷰티 >
	메이크업 > 베이스메이크업 > 메이크업베이스
	- text: 더툴랩 더스타일래쉬 4종리얼/내츄럴/볼륨/맥스 중 택1 003 볼륨 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품 >
	브로우관리 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품 > 브로우관리
	- text: Tweezerman 클래식 아이래쉬 컬러 속눈썹 뷰러 (#M)화장품/미용>뷰티소품>아이소품>눈썹칼 Naverstore > 화장품/미용
	> 뷰티소품 > 아이소품 > 눈썹칼
	- text: 에뛰드하우스 [에뛰드 추가쿠폰] 마이뷰티툴 속눈썹 3호 캣 아이 (#M)GSSHOP>뷰티>베이스메이크업>메이크업베이스 GSSHOP >
	뷰티 > 베이스메이크업 > 메이크업베이스
	- text: 미샤 시크릿 래쉬 - 1호 디어 (#M)홈>화장품/미용>뷰티소품>아이소품>속눈썹/속눈썹펌제 Naverstore > 화장품/미용 >
	뷰티소품 > 아이소품 > 속눈썹/속눈썹펌제
	inference: true
	model-index:
	- name: SetFit with klue/roberta-base
	results:
	- task:
	type: text-classification
	name: Text Classification
	dataset:
	name: Unknown
	type: unknown
	split: test
	metrics:
	- type: accuracy
	value: 0.9622489959839358
	name: Accuracy
	---

	# SetFit with klue/roberta-base

	This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [klue/roberta-base](https://huggingface.co/klue/roberta-base) 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: [klue/roberta-base](https://huggingface.co/klue/roberta-base)
	- 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 \|
	\|:------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 5 \| <ul><li>'[에뛰드] 마이뷰티툴 속눈썹 1ea 5호 홈>화장소품;홈>TOOL;(#M)홈>배송비 절약템 🛒 Naverstore > 화장품/미용 > 뷰티소품 > 아이소품 > 속눈썹/속눈썹펌제'</li><li>'더툴랩 더 스타일 래쉬 볼륨 TSL003 블랙 × 45개 (#M)쿠팡 홈>뷰티>뷰티소품>아이소품>속눈썹관리 소품 Coupang > 뷰티 > 뷰티소품 > 아이소품 > 속눈썹관리 소품'</li><li>'더툴랩 해피림 아이래쉬 내추럴 가닥속눈썹 1pack 11.5N (#M)화장품/미용>뷰티소품>아이소품>속눈썹/속눈썹펌제 Naverstore > 화장품/미용 > 뷰티소품 > 아이소품 > 속눈썹/속눈썹펌제'</li></ul> \|
	\| 1 \| <ul><li>'트위저맨 Tweezerman 스테인리스 브로우 쉐이핑 가위 및 브러시 521626 (#M)홈>화장품/미용>뷰티소품>헤어소품>미용가위 Naverstore > 화장품/미용 > 뷰티소품 > 헤어소품 > 미용가위'</li><li>'트위저맨 스테인리스 브로우 셰이핑 시져 브러쉬 70238 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 베이스/프라이머 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 베이스/프라이머'</li><li>'트위저맨 스테인리스 브로우 셰이핑 시져 브러쉬 70238 ssg > 뷰티 > 메이크업 > 베이스메이크업 > 파운데이션 ssg > 뷰티 > 메이크업 > 베이스메이크업 > 파운데이션'</li></ul> \|
	\| 0 \| <ul><li>'피카소 속눈썹 빗 피카소 속눈썹 빗 (#M)홈>미용소품>기타소품>기타미용소품 OLIVEYOUNG > 미용소품 > 기타소품 > 기타미용소품'</li><li>'트위저맨 프로페셔널 폴딩 아이래쉬콤브 1개 (#M)쿠팡 홈>뷰티>뷰티소품>페이스소품>브러쉬 Coupang > 뷰티 > 뷰티소품 > 페이스소품 > 브러쉬'</li><li>'속눈썹롯드 속눈썹펌롯드 L컬(SHARP) (#M)홈>화장품/미용>뷰티소품>아이소품>기타아이소품 Naverstore > 화장품/미용 > 뷰티소품 > 아이소품 > 기타아이소품'</li></ul> \|
	\| 3 \| <ul><li>'[Tweezerman] 트위저맨 클래식 속눈썹 뷰러 로즈 골드 1개입 (#M)홈>화장품/미용>뷰티소품>메이크업브러시>브러시세트 Naverstore > 화장품/미용 > 뷰티소품 > 메이크업브러시 > 브러시세트'</li><li>'[에뛰드] 마이 뷰티툴 뷰러 (1EA) (#M)홈>TOOL Naverstore > 화장품/미용 > 뷰티소품 > 아이소품 > 뷰러'</li><li>'시세이도 213뷰러 전체뷰러 속눈썹 고데기+뷰러리필 LotteOn > 뷰티 > 뷰티소품 > 아이소품 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품 > 속눈썹관리'</li></ul> \|
	\| 4 \| <ul><li>'e.l.f. 듀얼 펜슬 샤프너 10세트 (#M)쿠팡 홈>뷰티>뷰티소품>아이소품>족집게/샤프너 Coupang > 뷰티 > 뷰티소품 > 아이소품 > 족집게/샤프너'</li><li>'e.l.f. 듀얼 펜슬 샤프너 혼합 색상 × 6개입 (#M)쿠팡 홈>뷰티>뷰티소품>아이소품>족집게/샤프너 Coupang > 뷰티 > 뷰티소품 > 아이소품 > 족집게/샤프너'</li><li>'[맥]펜슬 샤프너 (#M)홈>화장품/미용>뷰티소품>아이소품>샤프너 Naverstore > 화장품/미용 > 뷰티소품 > 아이소품 > 샤프너'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.9622 \|

	## 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_bt6_3")
	# Run inference
	preds = model("Tweezerman 클래식 아이래쉬 컬러 속눈썹 뷰러 (#M)화장품/미용>뷰티소품>아이소품>눈썹칼 Naverstore > 화장품/미용 > 뷰티소품 > 아이소품 > 눈썹칼")
	```

	<!--
	### Downstream Use

	List how someone could finetune this model on their own dataset.
	-->

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	### Out-of-Scope Use

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	### Recommendations

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:--------\|:----\|
	\| Word count \| 13 \| 20.7017 \| 46 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0 \| 50 \|
	\| 1 \| 9 \|
	\| 3 \| 50 \|
	\| 4 \| 22 \|
	\| 5 \| 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0035 \| 1 \| 0.4788 \| - \|
	\| 0.1767 \| 50 \| 0.4758 \| - \|
	\| 0.3534 \| 100 \| 0.4629 \| - \|
	\| 0.5300 \| 150 \| 0.3827 \| - \|
	\| 0.7067 \| 200 \| 0.2023 \| - \|
	\| 0.8834 \| 250 \| 0.0454 \| - \|
	\| 1.0601 \| 300 \| 0.0031 \| - \|
	\| 1.2367 \| 350 \| 0.0001 \| - \|
	\| 1.4134 \| 400 \| 0.0001 \| - \|
	\| 1.5901 \| 450 \| 0.0 \| - \|
	\| 1.7668 \| 500 \| 0.0 \| - \|
	\| 1.9435 \| 550 \| 0.0001 \| - \|
	\| 2.1201 \| 600 \| 0.0 \| - \|
	\| 2.2968 \| 650 \| 0.0 \| - \|
	\| 2.4735 \| 700 \| 0.0 \| - \|
	\| 2.6502 \| 750 \| 0.0 \| - \|
	\| 2.8269 \| 800 \| 0.0 \| - \|
	\| 3.0035 \| 850 \| 0.0 \| - \|
	\| 3.1802 \| 900 \| 0.0 \| - \|
	\| 3.3569 \| 950 \| 0.0 \| - \|
	\| 3.5336 \| 1000 \| 0.0 \| - \|
	\| 3.7102 \| 1050 \| 0.0 \| - \|
	\| 3.8869 \| 1100 \| 0.0 \| - \|
	\| 4.0636 \| 1150 \| 0.0 \| - \|
	\| 4.2403 \| 1200 \| 0.0 \| - \|
	\| 4.4170 \| 1250 \| 0.0001 \| - \|
	\| 4.5936 \| 1300 \| 0.0012 \| - \|
	\| 4.7703 \| 1350 \| 0.0006 \| - \|
	\| 4.9470 \| 1400 \| 0.0 \| - \|
	\| 5.1237 \| 1450 \| 0.0 \| - \|
	\| 5.3004 \| 1500 \| 0.0 \| - \|
	\| 5.4770 \| 1550 \| 0.0 \| - \|
	\| 5.6537 \| 1600 \| 0.0 \| - \|
	\| 5.8304 \| 1650 \| 0.0 \| - \|
	\| 6.0071 \| 1700 \| 0.0 \| - \|
	\| 6.1837 \| 1750 \| 0.0 \| - \|
	\| 6.3604 \| 1800 \| 0.0 \| - \|
	\| 6.5371 \| 1850 \| 0.0 \| - \|
	\| 6.7138 \| 1900 \| 0.0 \| - \|
	\| 6.8905 \| 1950 \| 0.0 \| - \|
	\| 7.0671 \| 2000 \| 0.0 \| - \|
	\| 7.2438 \| 2050 \| 0.0 \| - \|
	\| 7.4205 \| 2100 \| 0.0 \| - \|
	\| 7.5972 \| 2150 \| 0.0 \| - \|
	\| 7.7739 \| 2200 \| 0.0 \| - \|
	\| 7.9505 \| 2250 \| 0.0 \| - \|
	\| 8.1272 \| 2300 \| 0.0 \| - \|
	\| 8.3039 \| 2350 \| 0.0 \| - \|
	\| 8.4806 \| 2400 \| 0.0 \| - \|
	\| 8.6572 \| 2450 \| 0.0 \| - \|
	\| 8.8339 \| 2500 \| 0.0 \| - \|
	\| 9.0106 \| 2550 \| 0.0 \| - \|
	\| 9.1873 \| 2600 \| 0.0 \| - \|
	\| 9.3640 \| 2650 \| 0.0 \| - \|
	\| 9.5406 \| 2700 \| 0.0 \| - \|
	\| 9.7173 \| 2750 \| 0.0 \| - \|
	\| 9.8940 \| 2800 \| 0.0 \| - \|
	\| 10.0707 \| 2850 \| 0.0 \| - \|
	\| 10.2473 \| 2900 \| 0.0 \| - \|
	\| 10.4240 \| 2950 \| 0.0 \| - \|
	\| 10.6007 \| 3000 \| 0.0 \| - \|
	\| 10.7774 \| 3050 \| 0.0 \| - \|
	\| 10.9541 \| 3100 \| 0.0 \| - \|
	\| 11.1307 \| 3150 \| 0.0 \| - \|
	\| 11.3074 \| 3200 \| 0.0 \| - \|
	\| 11.4841 \| 3250 \| 0.0 \| - \|
	\| 11.6608 \| 3300 \| 0.0 \| - \|
	\| 11.8375 \| 3350 \| 0.0 \| - \|
	\| 12.0141 \| 3400 \| 0.0 \| - \|
	\| 12.1908 \| 3450 \| 0.0 \| - \|
	\| 12.3675 \| 3500 \| 0.0 \| - \|
	\| 12.5442 \| 3550 \| 0.0 \| - \|
	\| 12.7208 \| 3600 \| 0.0 \| - \|
	\| 12.8975 \| 3650 \| 0.0 \| - \|
	\| 13.0742 \| 3700 \| 0.0 \| - \|
	\| 13.2509 \| 3750 \| 0.0 \| - \|
	\| 13.4276 \| 3800 \| 0.0 \| - \|
	\| 13.6042 \| 3850 \| 0.0 \| - \|
	\| 13.7809 \| 3900 \| 0.0 \| - \|
	\| 13.9576 \| 3950 \| 0.0 \| - \|
	\| 14.1343 \| 4000 \| 0.0 \| - \|
	\| 14.3110 \| 4050 \| 0.0 \| - \|
	\| 14.4876 \| 4100 \| 0.0 \| - \|
	\| 14.6643 \| 4150 \| 0.0 \| - \|
	\| 14.8410 \| 4200 \| 0.0 \| - \|
	\| 15.0177 \| 4250 \| 0.0 \| - \|
	\| 15.1943 \| 4300 \| 0.0 \| - \|
	\| 15.3710 \| 4350 \| 0.0 \| - \|
	\| 15.5477 \| 4400 \| 0.0 \| - \|
	\| 15.7244 \| 4450 \| 0.0 \| - \|
	\| 15.9011 \| 4500 \| 0.0 \| - \|
	\| 16.0777 \| 4550 \| 0.0 \| - \|
	\| 16.2544 \| 4600 \| 0.0 \| - \|
	\| 16.4311 \| 4650 \| 0.0 \| - \|
	\| 16.6078 \| 4700 \| 0.0 \| - \|
	\| 16.7845 \| 4750 \| 0.0 \| - \|
	\| 16.9611 \| 4800 \| 0.0 \| - \|
	\| 17.1378 \| 4850 \| 0.0 \| - \|
	\| 17.3145 \| 4900 \| 0.0 \| - \|
	\| 17.4912 \| 4950 \| 0.0 \| - \|
	\| 17.6678 \| 5000 \| 0.0 \| - \|
	\| 17.8445 \| 5050 \| 0.0 \| - \|
	\| 18.0212 \| 5100 \| 0.0 \| - \|
	\| 18.1979 \| 5150 \| 0.0 \| - \|
	\| 18.3746 \| 5200 \| 0.0 \| - \|
	\| 18.5512 \| 5250 \| 0.0 \| - \|
	\| 18.7279 \| 5300 \| 0.0 \| - \|
	\| 18.9046 \| 5350 \| 0.0 \| - \|
	\| 19.0813 \| 5400 \| 0.0 \| - \|
	\| 19.2580 \| 5450 \| 0.0 \| - \|
	\| 19.4346 \| 5500 \| 0.0 \| - \|
	\| 19.6113 \| 5550 \| 0.0 \| - \|
	\| 19.7880 \| 5600 \| 0.0 \| - \|
	\| 19.9647 \| 5650 \| 0.0 \| - \|
	\| 20.1413 \| 5700 \| 0.0 \| - \|
	\| 20.3180 \| 5750 \| 0.0 \| - \|
	\| 20.4947 \| 5800 \| 0.0 \| - \|
	\| 20.6714 \| 5850 \| 0.0 \| - \|
	\| 20.8481 \| 5900 \| 0.0 \| - \|
	\| 21.0247 \| 5950 \| 0.0 \| - \|
	\| 21.2014 \| 6000 \| 0.0 \| - \|
	\| 21.3781 \| 6050 \| 0.0 \| - \|
	\| 21.5548 \| 6100 \| 0.0 \| - \|
	\| 21.7314 \| 6150 \| 0.0 \| - \|
	\| 21.9081 \| 6200 \| 0.0 \| - \|
	\| 22.0848 \| 6250 \| 0.0 \| - \|
	\| 22.2615 \| 6300 \| 0.0 \| - \|
	\| 22.4382 \| 6350 \| 0.0 \| - \|
	\| 22.6148 \| 6400 \| 0.0 \| - \|
	\| 22.7915 \| 6450 \| 0.0 \| - \|
	\| 22.9682 \| 6500 \| 0.0 \| - \|
	\| 23.1449 \| 6550 \| 0.0 \| - \|
	\| 23.3216 \| 6600 \| 0.0 \| - \|
	\| 23.4982 \| 6650 \| 0.0 \| - \|
	\| 23.6749 \| 6700 \| 0.0 \| - \|
	\| 23.8516 \| 6750 \| 0.0 \| - \|
	\| 24.0283 \| 6800 \| 0.0 \| - \|
	\| 24.2049 \| 6850 \| 0.0 \| - \|
	\| 24.3816 \| 6900 \| 0.0 \| - \|
	\| 24.5583 \| 6950 \| 0.0 \| - \|
	\| 24.7350 \| 7000 \| 0.0 \| - \|
	\| 24.9117 \| 7050 \| 0.0 \| - \|
	\| 25.0883 \| 7100 \| 0.0 \| - \|
	\| 25.2650 \| 7150 \| 0.0 \| - \|
	\| 25.4417 \| 7200 \| 0.0 \| - \|
	\| 25.6184 \| 7250 \| 0.0 \| - \|
	\| 25.7951 \| 7300 \| 0.0 \| - \|
	\| 25.9717 \| 7350 \| 0.0 \| - \|
	\| 26.1484 \| 7400 \| 0.0 \| - \|
	\| 26.3251 \| 7450 \| 0.0 \| - \|
	\| 26.5018 \| 7500 \| 0.0 \| - \|
	\| 26.6784 \| 7550 \| 0.0 \| - \|
	\| 26.8551 \| 7600 \| 0.0 \| - \|
	\| 27.0318 \| 7650 \| 0.0 \| - \|
	\| 27.2085 \| 7700 \| 0.0 \| - \|
	\| 27.3852 \| 7750 \| 0.0 \| - \|
	\| 27.5618 \| 7800 \| 0.0 \| - \|
	\| 27.7385 \| 7850 \| 0.0 \| - \|
	\| 27.9152 \| 7900 \| 0.0 \| - \|
	\| 28.0919 \| 7950 \| 0.0 \| - \|
	\| 28.2686 \| 8000 \| 0.0 \| - \|
	\| 28.4452 \| 8050 \| 0.0 \| - \|
	\| 28.6219 \| 8100 \| 0.0 \| - \|
	\| 28.7986 \| 8150 \| 0.0 \| - \|
	\| 28.9753 \| 8200 \| 0.0 \| - \|
	\| 29.1519 \| 8250 \| 0.0 \| - \|
	\| 29.3286 \| 8300 \| 0.0 \| - \|
	\| 29.5053 \| 8350 \| 0.0 \| - \|
	\| 29.6820 \| 8400 \| 0.0 \| - \|
	\| 29.8587 \| 8450 \| 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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