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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: 마스크 오브 매그너민티 315g - 파워 마스크/페이스 앤 바디 마스크 팩 위메프 > 뷰티 > 바디/헤어 > 바디케어/워시/제모 >
	입욕제;위메프 > 뷰티 > 스킨케어 > 팩/마스크;위메프 > 뷰티 > 스킨케어 > 팩/마스크 > 워시오프팩 /필오프팩;위메프 > 뷰티 > 클렌징/필링
	> 클렌징;위메프 > 생활·주방·반려동물 > 바디/헤어 > 바디케어/워시/제모 > 입욕제;(#M)위메프 > 뷰티 > 스킨케어 > 팩/마스크
	> 마스크시트팩 위메프 > 뷰티 > 바디/헤어 > 바디케어/워시/제모 > 입욕제
	- text: '[대용량] 라네즈 크림 스킨 퀵 스킨 팩 100매(140ml) 피부진정 보습 (#M)홈>라네즈 Naverstore > 화장품/미용
	> 마스크/팩 > 수면팩'
	- text: 메디힐 티트리 케어솔루션 에센셜 마스크 이엑스 1매입 × 38개 LotteOn > 뷰티 > 스킨케어 > 마스크/팩 > 마스크팩 LotteOn
	> 뷰티 > 스킨케어 > 마스크/팩 > 마스크팩
	- text: 메디힐 마스크팩 티트리 베스트 10매 세트 수분 미백 여드름 비타 라이트빔 에센셜[10매] 홈>화장품/미용>마스크/팩>마스크시트;홈>전체상품;(#M)홈>브랜드관>메디힐
	Naverstore > 화장품/미용 > 마스크/팩 > 마스크시트
	- text: 메디힐 티트리 케어솔루션 에센셜 마스크 이엑스 1매입 × 29개 (#M)쿠팡 홈>뷰티>스킨케어>마스크/팩>시트마스크 Coupang >
	뷰티 > 스킨케어 > 마스크/팩 > 시트마스크
	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: 0.7775471698113208
	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: 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>'차앤박 CNP 안티포어 블랙헤드 클리어 키트 스트립 3세트(3회분) (#M)위메프 > 뷰티 > 스킨케어 > 팩/마스크 > 코팩 위메프 > 뷰티 > 스킨케어 > 팩/마스크 > 코팩'</li><li>'미팩토리 3단 돼지코팩 10개입 × 3개 (#M)쿠팡 홈>뷰티>스킨케어>마스크/팩>패치/코팩>코팩 Coupang > 뷰티 > 스킨케어 > 마스크/팩'</li><li>'[차앤박] CNP 안티포어 블랙헤드 버블 코팩 1매 / 넓은 모공 피부 / (#M)화장품/미용>마스크/팩>코팩 Naverstore > 화장품/미용 > 마스크/팩 > 코팩'</li></ul> \|
	\| 0 \| <ul><li>'메디힐×마리끌레르 기획전 앰플/크림/마스크팩~58% 25_메디힐 티트리 케어솔루션 에센셜마스크 [10매] 쇼킹딜 홈>뷰티>클렌징/팩/마스크>팩/마스크;11st>스킨케어>팩/마스크>마스크시트팩;(#M)11st>뷰티>클렌징/팩/마스크>팩/마스크 11st Hour Event > 패션/뷰티 > 뷰티 > 클렌징/팩/마스크 > 팩/마스크'</li><li>'[의료기기] 듀오덤 스팟패치 72매 [의료기기] 듀오덤 스팟패치 72매 (#M)홈>구강/건강용품>패치/겔>스팟패치 OLIVEYOUNG > 베스트 > 구강/건강용품'</li><li>'이지덤 뷰티 릴리프 스팟패치 57개입 3개 (#M)쿠팡 홈>생활용품>건강/의료용품>의약외품/상비용품>반창고/밴드 Coupang > 뷰티 > 스킨케어 > 마스크/팩 > 패치/코팩 > 스팟패치'</li></ul> \|
	\| 2 \| <ul><li>'안스킨 클래리파잉 골드 모델링 팩 1000ml 20개 (#M)홈>화장품/미용>마스크/팩>필오프팩 Naverstore > 화장품/미용 > 마스크/팩 > 필오프팩'</li><li>'[러쉬]오티픽스 75g - 프레쉬 페이스 마스크/마스크 팩 ssg > 뷰티 > 스킨케어 > 마스크/팩 > 시트마스크;ssg > 뷰티 > 헤어/바디 > 세정/입욕용품 > 입욕제/버블바스;ssg > 뷰티 > 스킨케어 > 마스크/팩;ssg > 뷰티 > 스킨케어 > 클렌징 ssg > 뷰티 > 스킨케어 > 마스크/팩 > 시트마스크'</li><li>'푸드어홀릭 콜라겐 필오프팩 150ml / 다시마 MinSellAmount (#M)화장품/향수>팩/마스크>필오프팩 Gmarket > 뷰티 > 화장품/향수 > 팩/마스크 > 필오프팩'</li></ul> \|
	\| 1 \| <ul><li>'물광 콜라겐 크림 티르티르 80ml 생크림 도자기 피부 물광마스크 이유빈 콜라겐물광마스크40ml (#M)홈>전체상품 Naverstore > 화장품/미용 > 남성화장품 > 크림'</li><li>'립 슬리핑 마스크 EX 20g 4종 베리 자몽 민트초코 애플라임 베리 (#M)홈>화장품/미용>마스크/팩>수면팩 Naverstore > 화장품/미용 > 마스크/팩 > 수면팩'</li><li>'설화수 한방 슬리핑마스크 나이트여운팩 120ml 1개 (#M)위메프 > 뷰티 > 스킨케어 > 팩/마스크 > 수면팩 위메프 > 뷰티 > 스킨케어 > 팩/마스크 > 수면팩'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.7775 \|

	## 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_item_top_bt3")
	# Run inference
	preds = model("[대용량] 라네즈 크림 스킨 퀵 스킨 팩 100매(140ml) 피부진정 보습 (#M)홈>라네즈 Naverstore > 화장품/미용 > 마스크/팩 > 수면팩")
	```

	<!--
	### 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 \| 21.75 \| 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 \| 0.4549 \| - \|
	\| 0.1597 \| 50 \| 0.3933 \| - \|
	\| 0.3195 \| 100 \| 0.3669 \| - \|
	\| 0.4792 \| 150 \| 0.2841 \| - \|
	\| 0.6390 \| 200 \| 0.1163 \| - \|
	\| 0.7987 \| 250 \| 0.0104 \| - \|
	\| 0.9585 \| 300 \| 0.0072 \| - \|
	\| 1.1182 \| 350 \| 0.0065 \| - \|
	\| 1.2780 \| 400 \| 0.0059 \| - \|
	\| 1.4377 \| 450 \| 0.0058 \| - \|
	\| 1.5974 \| 500 \| 0.0035 \| - \|
	\| 1.7572 \| 550 \| 0.0032 \| - \|
	\| 1.9169 \| 600 \| 0.0032 \| - \|
	\| 2.0767 \| 650 \| 0.0025 \| - \|
	\| 2.2364 \| 700 \| 0.0023 \| - \|
	\| 2.3962 \| 750 \| 0.0023 \| - \|
	\| 2.5559 \| 800 \| 0.0025 \| - \|
	\| 2.7157 \| 850 \| 0.0023 \| - \|
	\| 2.8754 \| 900 \| 0.003 \| - \|
	\| 3.0351 \| 950 \| 0.0026 \| - \|
	\| 3.1949 \| 1000 \| 0.0043 \| - \|
	\| 3.3546 \| 1050 \| 0.0022 \| - \|
	\| 3.5144 \| 1100 \| 0.0024 \| - \|
	\| 3.6741 \| 1150 \| 0.0025 \| - \|
	\| 3.8339 \| 1200 \| 0.0025 \| - \|
	\| 3.9936 \| 1250 \| 0.0024 \| - \|
	\| 4.1534 \| 1300 \| 0.0025 \| - \|
	\| 4.3131 \| 1350 \| 0.0025 \| - \|
	\| 4.4728 \| 1400 \| 0.0027 \| - \|
	\| 4.6326 \| 1450 \| 0.0023 \| - \|
	\| 4.7923 \| 1500 \| 0.0022 \| - \|
	\| 4.9521 \| 1550 \| 0.0026 \| - \|
	\| 5.1118 \| 1600 \| 0.0022 \| - \|
	\| 5.2716 \| 1650 \| 0.0027 \| - \|
	\| 5.4313 \| 1700 \| 0.0022 \| - \|
	\| 5.5911 \| 1750 \| 0.0024 \| - \|
	\| 5.7508 \| 1800 \| 0.0029 \| - \|
	\| 5.9105 \| 1850 \| 0.0018 \| - \|
	\| 6.0703 \| 1900 \| 0.0033 \| - \|
	\| 6.2300 \| 1950 \| 0.002 \| - \|
	\| 6.3898 \| 2000 \| 0.0027 \| - \|
	\| 6.5495 \| 2050 \| 0.0021 \| - \|
	\| 6.7093 \| 2100 \| 0.0022 \| - \|
	\| 6.8690 \| 2150 \| 0.0023 \| - \|
	\| 7.0288 \| 2200 \| 0.0026 \| - \|
	\| 7.1885 \| 2250 \| 0.0018 \| - \|
	\| 7.3482 \| 2300 \| 0.0024 \| - \|
	\| 7.5080 \| 2350 \| 0.002 \| - \|
	\| 7.6677 \| 2400 \| 0.0027 \| - \|
	\| 7.8275 \| 2450 \| 0.0022 \| - \|
	\| 7.9872 \| 2500 \| 0.0032 \| - \|
	\| 8.1470 \| 2550 \| 0.0029 \| - \|
	\| 8.3067 \| 2600 \| 0.0025 \| - \|
	\| 8.4665 \| 2650 \| 0.0017 \| - \|
	\| 8.6262 \| 2700 \| 0.0026 \| - \|
	\| 8.7859 \| 2750 \| 0.0023 \| - \|
	\| 8.9457 \| 2800 \| 0.0023 \| - \|
	\| 9.1054 \| 2850 \| 0.0029 \| - \|
	\| 9.2652 \| 2900 \| 0.0028 \| - \|
	\| 9.4249 \| 2950 \| 0.0021 \| - \|
	\| 9.5847 \| 3000 \| 0.0027 \| - \|
	\| 9.7444 \| 3050 \| 0.0019 \| - \|
	\| 9.9042 \| 3100 \| 0.0022 \| - \|
	\| 10.0639 \| 3150 \| 0.003 \| - \|
	\| 10.2236 \| 3200 \| 0.0024 \| - \|
	\| 10.3834 \| 3250 \| 0.0019 \| - \|
	\| 10.5431 \| 3300 \| 0.0023 \| - \|
	\| 10.7029 \| 3350 \| 0.0024 \| - \|
	\| 10.8626 \| 3400 \| 0.0026 \| - \|
	\| 11.0224 \| 3450 \| 0.0025 \| - \|
	\| 11.1821 \| 3500 \| 0.0022 \| - \|
	\| 11.3419 \| 3550 \| 0.0023 \| - \|
	\| 11.5016 \| 3600 \| 0.0027 \| - \|
	\| 11.6613 \| 3650 \| 0.0032 \| - \|
	\| 11.8211 \| 3700 \| 0.0022 \| - \|
	\| 11.9808 \| 3750 \| 0.0019 \| - \|
	\| 12.1406 \| 3800 \| 0.0029 \| - \|
	\| 12.3003 \| 3850 \| 0.0026 \| - \|
	\| 12.4601 \| 3900 \| 0.0027 \| - \|
	\| 12.6198 \| 3950 \| 0.0019 \| - \|
	\| 12.7796 \| 4000 \| 0.0021 \| - \|
	\| 12.9393 \| 4050 \| 0.0023 \| - \|
	\| 13.0990 \| 4100 \| 0.0027 \| - \|
	\| 13.2588 \| 4150 \| 0.0021 \| - \|
	\| 13.4185 \| 4200 \| 0.0022 \| - \|
	\| 13.5783 \| 4250 \| 0.0026 \| - \|
	\| 13.7380 \| 4300 \| 0.0025 \| - \|
	\| 13.8978 \| 4350 \| 0.0025 \| - \|
	\| 14.0575 \| 4400 \| 0.0021 \| - \|
	\| 14.2173 \| 4450 \| 0.0031 \| - \|
	\| 14.3770 \| 4500 \| 0.0022 \| - \|
	\| 14.5367 \| 4550 \| 0.0016 \| - \|
	\| 14.6965 \| 4600 \| 0.0027 \| - \|
	\| 14.8562 \| 4650 \| 0.0027 \| - \|
	\| 15.0160 \| 4700 \| 0.0027 \| - \|
	\| 15.1757 \| 4750 \| 0.0021 \| - \|
	\| 15.3355 \| 4800 \| 0.0027 \| - \|
	\| 15.4952 \| 4850 \| 0.0031 \| - \|
	\| 15.6550 \| 4900 \| 0.0021 \| - \|
	\| 15.8147 \| 4950 \| 0.0023 \| - \|
	\| 15.9744 \| 5000 \| 0.002 \| - \|
	\| 16.1342 \| 5050 \| 0.0024 \| - \|
	\| 16.2939 \| 5100 \| 0.0026 \| - \|
	\| 16.4537 \| 5150 \| 0.002 \| - \|
	\| 16.6134 \| 5200 \| 0.0026 \| - \|
	\| 16.7732 \| 5250 \| 0.0029 \| - \|
	\| 16.9329 \| 5300 \| 0.0023 \| - \|
	\| 17.0927 \| 5350 \| 0.0022 \| - \|
	\| 17.2524 \| 5400 \| 0.0028 \| - \|
	\| 17.4121 \| 5450 \| 0.0026 \| - \|
	\| 17.5719 \| 5500 \| 0.0017 \| - \|
	\| 17.7316 \| 5550 \| 0.0032 \| - \|
	\| 17.8914 \| 5600 \| 0.0022 \| - \|
	\| 18.0511 \| 5650 \| 0.0019 \| - \|
	\| 18.2109 \| 5700 \| 0.0024 \| - \|
	\| 18.3706 \| 5750 \| 0.0026 \| - \|
	\| 18.5304 \| 5800 \| 0.0031 \| - \|
	\| 18.6901 \| 5850 \| 0.0024 \| - \|
	\| 18.8498 \| 5900 \| 0.0018 \| - \|
	\| 19.0096 \| 5950 \| 0.0023 \| - \|
	\| 19.1693 \| 6000 \| 0.0025 \| - \|
	\| 19.3291 \| 6050 \| 0.0028 \| - \|
	\| 19.4888 \| 6100 \| 0.002 \| - \|
	\| 19.6486 \| 6150 \| 0.0026 \| - \|
	\| 19.8083 \| 6200 \| 0.0022 \| - \|
	\| 19.9681 \| 6250 \| 0.0025 \| - \|
	\| 20.1278 \| 6300 \| 0.0022 \| - \|
	\| 20.2875 \| 6350 \| 0.0025 \| - \|
	\| 20.4473 \| 6400 \| 0.0024 \| - \|
	\| 20.6070 \| 6450 \| 0.0027 \| - \|
	\| 20.7668 \| 6500 \| 0.0017 \| - \|
	\| 20.9265 \| 6550 \| 0.0025 \| - \|
	\| 21.0863 \| 6600 \| 0.0025 \| - \|
	\| 21.2460 \| 6650 \| 0.002 \| - \|
	\| 21.4058 \| 6700 \| 0.0033 \| - \|
	\| 21.5655 \| 6750 \| 0.0021 \| - \|
	\| 21.7252 \| 6800 \| 0.0022 \| - \|
	\| 21.8850 \| 6850 \| 0.0027 \| - \|
	\| 22.0447 \| 6900 \| 0.0021 \| - \|
	\| 22.2045 \| 6950 \| 0.0028 \| - \|
	\| 22.3642 \| 7000 \| 0.0021 \| - \|
	\| 22.5240 \| 7050 \| 0.0021 \| - \|
	\| 22.6837 \| 7100 \| 0.0027 \| - \|
	\| 22.8435 \| 7150 \| 0.0021 \| - \|
	\| 23.0032 \| 7200 \| 0.0029 \| - \|
	\| 23.1629 \| 7250 \| 0.0036 \| - \|
	\| 23.3227 \| 7300 \| 0.002 \| - \|
	\| 23.4824 \| 7350 \| 0.0021 \| - \|
	\| 23.6422 \| 7400 \| 0.002 \| - \|
	\| 23.8019 \| 7450 \| 0.0025 \| - \|
	\| 23.9617 \| 7500 \| 0.0024 \| - \|
	\| 24.1214 \| 7550 \| 0.0026 \| - \|
	\| 24.2812 \| 7600 \| 0.002 \| - \|
	\| 24.4409 \| 7650 \| 0.0024 \| - \|
	\| 24.6006 \| 7700 \| 0.0025 \| - \|
	\| 24.7604 \| 7750 \| 0.0023 \| - \|
	\| 24.9201 \| 7800 \| 0.0027 \| - \|
	\| 25.0799 \| 7850 \| 0.0023 \| - \|
	\| 25.2396 \| 7900 \| 0.0024 \| - \|
	\| 25.3994 \| 7950 \| 0.0027 \| - \|
	\| 25.5591 \| 8000 \| 0.0038 \| - \|
	\| 25.7188 \| 8050 \| 0.0065 \| - \|
	\| 25.8786 \| 8100 \| 0.0037 \| - \|
	\| 26.0383 \| 8150 \| 0.0032 \| - \|
	\| 26.1981 \| 8200 \| 0.0031 \| - \|
	\| 26.3578 \| 8250 \| 0.0028 \| - \|
	\| 26.5176 \| 8300 \| 0.0024 \| - \|
	\| 26.6773 \| 8350 \| 0.0023 \| - \|
	\| 26.8371 \| 8400 \| 0.0028 \| - \|
	\| 26.9968 \| 8450 \| 0.0023 \| - \|
	\| 27.1565 \| 8500 \| 0.0028 \| - \|
	\| 27.3163 \| 8550 \| 0.0025 \| - \|
	\| 27.4760 \| 8600 \| 0.0027 \| - \|
	\| 27.6358 \| 8650 \| 0.002 \| - \|
	\| 27.7955 \| 8700 \| 0.0024 \| - \|
	\| 27.9553 \| 8750 \| 0.0023 \| - \|
	\| 28.1150 \| 8800 \| 0.0029 \| - \|
	\| 28.2748 \| 8850 \| 0.0025 \| - \|
	\| 28.4345 \| 8900 \| 0.002 \| - \|
	\| 28.5942 \| 8950 \| 0.0025 \| - \|
	\| 28.7540 \| 9000 \| 0.002 \| - \|
	\| 28.9137 \| 9050 \| 0.0027 \| - \|
	\| 29.0735 \| 9100 \| 0.0028 \| - \|
	\| 29.2332 \| 9150 \| 0.0016 \| - \|
	\| 29.3930 \| 9200 \| 0.0032 \| - \|
	\| 29.5527 \| 9250 \| 0.0026 \| - \|
	\| 29.7125 \| 9300 \| 0.0025 \| - \|
	\| 29.8722 \| 9350 \| 0.0025 \| - \|

	### 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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