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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: 듄 포 맨 오 드 뚜왈렛 (100ml) LotteOn > 뷰티 > 향수 > 남성향수 LotteOn > 뷰티 > 향수 > 남성향수
	- text: 오랑쥬 상긴느 200ml (증정) 울랑 앙피니 30ml_마젠타 LotteOn > 뷰티 > 베이스메이크업 > 향수/디퓨저 > 공용향수
	LotteOn > 뷰티 > 명품화장품 > 향수/디퓨저 > 공용향수
	- text: 디올 블루밍 부케 롤러 펄 오 드 뚜왈렛 20ml LotteOn > 뷰티 > 향수 > 여성향수 LotteOn > 뷰티 > 향수 >
	여성향수
	- text: 포멜로 파라디 30ml +1.7ml 1종 마젠타 LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded
	> 아틀리에 코롱 DepartmentLotteOn > 뷰티 > 향수 > 향수세트
	- text: 베르가모트 솔레이 200ml (증정) 울랑 앙피니 30ml_블랙 LOREAL > DepartmentLotteOn > 아틀리에 코롱 >
	Branded > 아틀리에 코롱 LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded > 아틀리에 코롱
	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: 0.5334819796768769
	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>'마타바 석고방향제 만들기 diy 재료모음 05_석고전용색소_01_석고전용색소50ml_노랑 (#M)11st>과자/간식>초콜릿>초콜릿DIY>도구 기타 11st > 식품 > 과자/간식 > 초콜릿 > 초콜릿DIY'</li><li>'A 시그니처 디퓨저 1+1 프로모션 네롤리바질_피오니 LotteOn > 생활/건강 > 세제/방향/살충 > 방향제 LotteOn > 생활/건강 > 세제/방향/살충 > 방향제'</li><li>'마타바 석고방향제 만들기 diy 재료모음 01_스위스G향료100ml_멋스럽고세련된향기_69_인투유 (#M)11st>과자/간식>초콜릿>초콜릿DIY>도구 기타 11st > 식품 > 과자/간식 > 초콜릿 > 초콜릿DIY'</li></ul> \|
	\| 0 \| <ul><li>'아틀리에 코롱 - 자스민 안젤리크 코롱 압솔뤼 스프레이 100ml/3.3oz LOREAL > Ssg > 아틀리에 코롱 > Branded > 아틀리에 코롱 LOREAL > Ssg > 아틀리에 코롱 > Branded > 아틀리에 코롱'</li><li>'베티베르 파탈 200ml (증정) 아이리스 리벨 30ml LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded > 아틀리에 코롱 LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded > 아틀리에 코롱'</li><li>'포멜로 파라디 30ml +1.7ml 1종 코랄 LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded > 아틀리에 코롱 DepartmentLotteOn > 뷰티 > 향수 > 향수세트'</li></ul> \|
	\| 2 \| <ul><li>'베르가모트 솔레이 200ml (증정) 러브 오스만투스 30ml_오랑쥬 LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded > 아틀리에 코롱 LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded > 아틀리에 코롱'</li><li>'포멜로 파라디 100ml 코랄 LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded > 포멜로 파라디 LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded > 포멜로 파라디'</li><li>'베르가모트 솔레이 200ml (증정) 클레망틴 캘리포니아 30ml_마젠타 LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded > 아틀리에 코롱 LOREAL > DepartmentLotteOn > 아틀리에 코롱 > Branded > 아틀리에 코롱'</li></ul> \|
	\| 1 \| <ul><li>'불가리 뿌르 옴므 익스트림 EDT 50ml LotteOn > 뷰티 > 향수 > 남성향수 LotteOn > 뷰티 > 향수 > 남성향수'</li><li>'조르지오 아르마니 아쿠아 디 지오 옴므 세트 EDT 100ml + 트레블 15ml (#M)위메프 > 뷰티 > 명품화장품 > 메이크업 > 립메이크업 위메프 > 뷰티 > 명품화장품 > 스킨케어'</li><li>'불가리 뿌르옴므 익스트림 100ml 50ml 30ml 백화점정품 50ml 백화점정품 홈>화장품/미용>향수>남성향수;(#M)홈>남자향수>불가리 Naverstore > 화장품/미용 > 향수 > 남성향수'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.5335 \|

	## 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_bt11")
	# Run inference
	preds = model("듄 포 맨 오 드 뚜왈렛 (100ml) LotteOn > 뷰티 > 향수 > 남성향수 LotteOn > 뷰티 > 향수 > 남성향수")
	```

	<!--
	### Downstream Use

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

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	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
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	<!--
	## 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.
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	### Recommendations

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 11 \| 26.41 \| 45 \|

	\| 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 \| 0.395 \| - \|
	\| 0.1597 \| 50 \| 0.3286 \| - \|
	\| 0.3195 \| 100 \| 0.2663 \| - \|
	\| 0.4792 \| 150 \| 0.2215 \| - \|
	\| 0.6390 \| 200 \| 0.1928 \| - \|
	\| 0.7987 \| 250 \| 0.081 \| - \|
	\| 0.9585 \| 300 \| 0.0147 \| - \|
	\| 1.1182 \| 350 \| 0.0027 \| - \|
	\| 1.2780 \| 400 \| 0.0008 \| - \|
	\| 1.4377 \| 450 \| 0.0004 \| - \|
	\| 1.5974 \| 500 \| 0.0006 \| - \|
	\| 1.7572 \| 550 \| 0.0003 \| - \|
	\| 1.9169 \| 600 \| 0.0001 \| - \|
	\| 2.0767 \| 650 \| 0.0001 \| - \|
	\| 2.2364 \| 700 \| 0.0001 \| - \|
	\| 2.3962 \| 750 \| 0.0001 \| - \|
	\| 2.5559 \| 800 \| 0.0 \| - \|
	\| 2.7157 \| 850 \| 0.0 \| - \|
	\| 2.8754 \| 900 \| 0.0 \| - \|
	\| 3.0351 \| 950 \| 0.0 \| - \|
	\| 3.1949 \| 1000 \| 0.0 \| - \|
	\| 3.3546 \| 1050 \| 0.0 \| - \|
	\| 3.5144 \| 1100 \| 0.0 \| - \|
	\| 3.6741 \| 1150 \| 0.0 \| - \|
	\| 3.8339 \| 1200 \| 0.0 \| - \|
	\| 3.9936 \| 1250 \| 0.0 \| - \|
	\| 4.1534 \| 1300 \| 0.0 \| - \|
	\| 4.3131 \| 1350 \| 0.0 \| - \|
	\| 4.4728 \| 1400 \| 0.0 \| - \|
	\| 4.6326 \| 1450 \| 0.0 \| - \|
	\| 4.7923 \| 1500 \| 0.0 \| - \|
	\| 4.9521 \| 1550 \| 0.0 \| - \|
	\| 5.1118 \| 1600 \| 0.0 \| - \|
	\| 5.2716 \| 1650 \| 0.0 \| - \|
	\| 5.4313 \| 1700 \| 0.0 \| - \|
	\| 5.5911 \| 1750 \| 0.0 \| - \|
	\| 5.7508 \| 1800 \| 0.0 \| - \|
	\| 5.9105 \| 1850 \| 0.0 \| - \|
	\| 6.0703 \| 1900 \| 0.0 \| - \|
	\| 6.2300 \| 1950 \| 0.0 \| - \|
	\| 6.3898 \| 2000 \| 0.0 \| - \|
	\| 6.5495 \| 2050 \| 0.0 \| - \|
	\| 6.7093 \| 2100 \| 0.0 \| - \|
	\| 6.8690 \| 2150 \| 0.0 \| - \|
	\| 7.0288 \| 2200 \| 0.0 \| - \|
	\| 7.1885 \| 2250 \| 0.0 \| - \|
	\| 7.3482 \| 2300 \| 0.0 \| - \|
	\| 7.5080 \| 2350 \| 0.0 \| - \|
	\| 7.6677 \| 2400 \| 0.0 \| - \|
	\| 7.8275 \| 2450 \| 0.0 \| - \|
	\| 7.9872 \| 2500 \| 0.0 \| - \|
	\| 8.1470 \| 2550 \| 0.0 \| - \|
	\| 8.3067 \| 2600 \| 0.0 \| - \|
	\| 8.4665 \| 2650 \| 0.0 \| - \|
	\| 8.6262 \| 2700 \| 0.0 \| - \|
	\| 8.7859 \| 2750 \| 0.0 \| - \|
	\| 8.9457 \| 2800 \| 0.0 \| - \|
	\| 9.1054 \| 2850 \| 0.0 \| - \|
	\| 9.2652 \| 2900 \| 0.0 \| - \|
	\| 9.4249 \| 2950 \| 0.0 \| - \|
	\| 9.5847 \| 3000 \| 0.0 \| - \|
	\| 9.7444 \| 3050 \| 0.0 \| - \|
	\| 9.9042 \| 3100 \| 0.0 \| - \|
	\| 10.0639 \| 3150 \| 0.0 \| - \|
	\| 10.2236 \| 3200 \| 0.0 \| - \|
	\| 10.3834 \| 3250 \| 0.0 \| - \|
	\| 10.5431 \| 3300 \| 0.0 \| - \|
	\| 10.7029 \| 3350 \| 0.0 \| - \|
	\| 10.8626 \| 3400 \| 0.0 \| - \|
	\| 11.0224 \| 3450 \| 0.0 \| - \|
	\| 11.1821 \| 3500 \| 0.0 \| - \|
	\| 11.3419 \| 3550 \| 0.0 \| - \|
	\| 11.5016 \| 3600 \| 0.0 \| - \|
	\| 11.6613 \| 3650 \| 0.0 \| - \|
	\| 11.8211 \| 3700 \| 0.0 \| - \|
	\| 11.9808 \| 3750 \| 0.0 \| - \|
	\| 12.1406 \| 3800 \| 0.0 \| - \|
	\| 12.3003 \| 3850 \| 0.0 \| - \|
	\| 12.4601 \| 3900 \| 0.0 \| - \|
	\| 12.6198 \| 3950 \| 0.0 \| - \|
	\| 12.7796 \| 4000 \| 0.0 \| - \|
	\| 12.9393 \| 4050 \| 0.0 \| - \|
	\| 13.0990 \| 4100 \| 0.0 \| - \|
	\| 13.2588 \| 4150 \| 0.0 \| - \|
	\| 13.4185 \| 4200 \| 0.0 \| - \|
	\| 13.5783 \| 4250 \| 0.0 \| - \|
	\| 13.7380 \| 4300 \| 0.0 \| - \|
	\| 13.8978 \| 4350 \| 0.0 \| - \|
	\| 14.0575 \| 4400 \| 0.0 \| - \|
	\| 14.2173 \| 4450 \| 0.0 \| - \|
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	\| 15.9744 \| 5000 \| 0.0 \| - \|
	\| 16.1342 \| 5050 \| 0.0 \| - \|
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	\| 19.1693 \| 6000 \| 0.0 \| - \|
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	\| 19.6486 \| 6150 \| 0.0 \| - \|
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	\| 21.8850 \| 6850 \| 0.0 \| - \|
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	\| 22.3642 \| 7000 \| 0.0 \| - \|
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	\| 22.8435 \| 7150 \| 0.0 \| - \|
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	\| 23.4824 \| 7350 \| 0.0 \| - \|
	\| 23.6422 \| 7400 \| 0.0 \| - \|
	\| 23.8019 \| 7450 \| 0.0 \| - \|
	\| 23.9617 \| 7500 \| 0.0 \| - \|
	\| 24.1214 \| 7550 \| 0.0 \| - \|
	\| 24.2812 \| 7600 \| 0.0 \| - \|
	\| 24.4409 \| 7650 \| 0.0 \| - \|
	\| 24.6006 \| 7700 \| 0.0 \| - \|
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	\| 24.9201 \| 7800 \| 0.0 \| - \|
	\| 25.0799 \| 7850 \| 0.0 \| - \|
	\| 25.2396 \| 7900 \| 0.0 \| - \|
	\| 25.3994 \| 7950 \| 0.0 \| - \|
	\| 25.5591 \| 8000 \| 0.0 \| - \|
	\| 25.7188 \| 8050 \| 0.0 \| - \|
	\| 25.8786 \| 8100 \| 0.0 \| - \|
	\| 26.0383 \| 8150 \| 0.0 \| - \|
	\| 26.1981 \| 8200 \| 0.0 \| - \|
	\| 26.3578 \| 8250 \| 0.0 \| - \|
	\| 26.5176 \| 8300 \| 0.0 \| - \|
	\| 26.6773 \| 8350 \| 0.0 \| - \|
	\| 26.8371 \| 8400 \| 0.0 \| - \|
	\| 26.9968 \| 8450 \| 0.0 \| - \|
	\| 27.1565 \| 8500 \| 0.0 \| - \|
	\| 27.3163 \| 8550 \| 0.0 \| - \|
	\| 27.4760 \| 8600 \| 0.0 \| - \|
	\| 27.6358 \| 8650 \| 0.0 \| - \|
	\| 27.7955 \| 8700 \| 0.0 \| - \|
	\| 27.9553 \| 8750 \| 0.0 \| - \|
	\| 28.1150 \| 8800 \| 0.0 \| - \|
	\| 28.2748 \| 8850 \| 0.0 \| - \|
	\| 28.4345 \| 8900 \| 0.0 \| - \|
	\| 28.5942 \| 8950 \| 0.0 \| - \|
	\| 28.7540 \| 9000 \| 0.0 \| - \|
	\| 28.9137 \| 9050 \| 0.0 \| - \|
	\| 29.0735 \| 9100 \| 0.0 \| - \|
	\| 29.2332 \| 9150 \| 0.0 \| - \|
	\| 29.3930 \| 9200 \| 0.0 \| - \|
	\| 29.5527 \| 9250 \| 0.0 \| - \|
	\| 29.7125 \| 9300 \| 0.0 \| - \|
	\| 29.8722 \| 9350 \| 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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