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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: '[당일출고] 비플레인 녹두 약산성 클렌징폼 160ml 옵션없음 제이에이치컴퍼니'
	- text: 아임프롬 피그 스크럽 마스크, 120g, 1개 120g × 1개 120g x 1개 익사이팅
	- text: 코스트코 오스트레일리안 보태니컬비누 버라이어티팩 200g x 8개 목욕비누 1. 고트밀크&레몬그라스 외 3종 8개 굿바이즈
	- text: 바로출고 로자그라프 망고클렌징젤 500ml 열감많은 촉촉 깔끔 클렌징젤 공병 [추가로 필요한 경우] 클렌징젤 사각공병 후니맘
	- text: 바이오뷰텍 바이오옵틱스 아이크린 리드 클리너 30매 1021542 옵션없음 페이즈
	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: 0.6902857142857143
	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: 12 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 \|
	\|:------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 5.0 \| <ul><li>'시세이도 티스 딥 오프 오일 280ml/딥클렌징오일/ 옵션없음 디바1004'</li><li>"[1+1] 달바 비건 세럼 클렌저 펌프형 150mlX2개 [세트] 세럼클렌저'펌프형' 150ml(2개) 주식회사 달바글로벌"</li><li>'비플레인 클렌징 오일 순한 녹두 클렌징 오일 200ml 옵션없음 베나 스토어'</li></ul> \|
	\| 9.0 \| <ul><li>'토니모리 더 촉촉 그린티 노-워시 클렌징 티슈 100매 옵션없음 (SJ)이커머스'</li><li>'[비욘드] 라이스밀크 마일드딥클렌징티슈 50 매 옵션없음 (주)엘지생활건강'</li><li>'코스알엑스 원스텝 모이스쳐 업 패드 70매 140ml 옵션없음 주식회사 다올연구소'</li></ul> \|
	\| 3.0 \| <ul><li>'히노끼 퓨어바 식물나라 100g 4입 3개 옵션없음 이프니드'</li><li>'두레생협 수제온가족보습비누 옵션없음 이프니드'</li><li>'[코스트코] 아이보리 오리지널 비누 (113g, 10개) 옵션없음 아담상회(주)'</li></ul> \|
	\| 10.0 \| <ul><li>'인셀덤 엑티브 클린업 파우더 90g 래디언솜 앰플 아이코스메틱'</li><li>'[정품보장] 인셀덤 엑티브 클린업 파우더 90g 옵션없음 신우유통'</li><li>'동국제약 센텔리안24 마데카 엔자임 클렌징 파우더 60g x 1개 동국제약 센텔리안'</li></ul> \|
	\| 2.0 \| <ul><li>'1개 청미정 알로에 발효 클렌징밀크 옵션없음 주식회사 모아박스'</li><li>'1개 청미정 알로에 발효 클렌징 밀크 옵션없음 주식회사 모아박스'</li><li>'피토메르 레데마끼앙 클렌징밀크 250ml+해면 옵션없음 주식회사 알래스카(ALASKA)'</li></ul> \|
	\| 0.0 \| <ul><li>'오큐소프트 리드스크럽 플러스 눈꺼풀세정제 눈세척 눈청소 눈기름샘 마이봄샘 다래끼 아이클린 11203414 오큐소프트 리드스크럽플러스 오큐소프트 리드스크럽플러스 메이써니'</li><li>'해서린 선셋 원킬 리무버 패드, 170ml, 1개 옵션없음 건강드림'</li><li>'바닐라코 립앤아이 리무버 클리어 100ml 옵션없음 뉴베이스'</li></ul> \|
	\| 11.0 \| <ul><li>'마미레시피 황유자 고마쥬 클렌저 100ml 1개 옵션없음 건강드림'</li><li>'폰즈 딥클렌징폼 스파 200ml 1+1 [00001] 없음 디휴니'</li><li>'바이오가 판테놀 클렌징폼 500ml 옵션없음 잡(Job)상인'</li></ul> \|
	\| 8.0 \| <ul><li>'MP 크린징 크림 순한 클렌저 화장 지우기 촉촉한 클렌징 노폐물제거 300ml 묵은각질 옵션없음 민트펌킨'</li><li>'과일나라 첫물녹차 프레시 클렌징 크림 x3개 옵션없음 뷰티디자인'</li><li>'파인앤유 녹차 클렌징 크림 500g 딥클렌징 옵션없음 파인앤유'</li></ul> \|
	\| 6.0 \| <ul><li>'바이오더마 센시비오 H2O 500ml 옵션없음 브이브이에스'</li><li>'브링그린 티트리시카센시티브클렌징워터500mL_NEW 브링그린 티트리시카센시티브클렌징워터500mL_ 로프트'</li><li>'[아일릿 민주PICK]바이오더마 하이드라비오 H2O 500ml 옵션없음 브이브이에스'</li></ul> \|
	\| 7.0 \| <ul><li>'코스알엑스 약산성 굿모닝 젤 클렌저 150ml 9792539 옵션없음 에필로리아'</li><li>'벨프리모 안티크네 젤클렌져 안티크네 세범 리듀서(토너) 300ml 주식회사 소통'</li><li>'라로슈포제 시카플라스트 라방 B5 200ml 옵션없음 주식회사 엠디글로벌'</li></ul> \|
	\| 4.0 \| <ul><li>'[EVENT] 블랑루스 나이트루틴 클렌징세트 블랑루스 공식몰'</li><li>'[갤러리아] [OVIS] SHEEPS MILK SOAP GRAPEFRUITS ALGAE 옵션없음 한화갤러리아(주)'</li><li>'디오메르 페이셜 클렌징 세트 디오메르'</li></ul> \|
	\| 1.0 \| <ul><li>'[Vegan] 아임프롬 피그 스크럽 마스크 120g +피그 스크럽 마스크 30g 옵션없음 랩앤컴퍼니(주)'</li><li>'셀리맥스 바디 브라이트닝 패드 60매 / 색소 침착 미백 케어 팔꿈치 무릎 바디 브라이트닝 패드 1개 (주)앱솔브랩'</li><li>'메디필 엑스트라 슈퍼9 플러스 2.0 기획 (피지연화제) 옵션없음 덩이네'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.6903 \|

	## 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_bt9_test")
	# Run inference
	preds = model("[당일출고] 비플레인 녹두 약산성 클렌징폼 160ml 옵션없음 제이에이치컴퍼니")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 3 \| 9.0359 \| 19 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0.0 \| 20 \|
	\| 1.0 \| 27 \|
	\| 2.0 \| 20 \|
	\| 3.0 \| 27 \|
	\| 4.0 \| 15 \|
	\| 5.0 \| 20 \|
	\| 6.0 \| 20 \|
	\| 7.0 \| 18 \|
	\| 8.0 \| 20 \|
	\| 9.0 \| 22 \|
	\| 10.0 \| 17 \|
	\| 11.0 \| 25 \|

	### Training Hyperparameters
	- batch_size: (512, 512)
	- num_epochs: (50, 50)
	- max_steps: -1
	- sampling_strategy: oversampling
	- num_iterations: 60
	- 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0333 \| 1 \| 0.4927 \| - \|
	\| 1.6667 \| 50 \| 0.4009 \| - \|
	\| 3.3333 \| 100 \| 0.1238 \| - \|
	\| 5.0 \| 150 \| 0.0523 \| - \|
	\| 6.6667 \| 200 \| 0.0156 \| - \|
	\| 8.3333 \| 250 \| 0.0022 \| - \|
	\| 10.0 \| 300 \| 0.0003 \| - \|
	\| 11.6667 \| 350 \| 0.0002 \| - \|
	\| 13.3333 \| 400 \| 0.0002 \| - \|
	\| 15.0 \| 450 \| 0.0001 \| - \|
	\| 16.6667 \| 500 \| 0.0001 \| - \|
	\| 18.3333 \| 550 \| 0.0001 \| - \|
	\| 20.0 \| 600 \| 0.0001 \| - \|
	\| 21.6667 \| 650 \| 0.0001 \| - \|
	\| 23.3333 \| 700 \| 0.0001 \| - \|
	\| 25.0 \| 750 \| 0.0001 \| - \|
	\| 26.6667 \| 800 \| 0.0001 \| - \|
	\| 28.3333 \| 850 \| 0.0001 \| - \|
	\| 30.0 \| 900 \| 0.0001 \| - \|
	\| 31.6667 \| 950 \| 0.0001 \| - \|
	\| 33.3333 \| 1000 \| 0.0001 \| - \|
	\| 35.0 \| 1050 \| 0.0001 \| - \|
	\| 36.6667 \| 1100 \| 0.0001 \| - \|
	\| 38.3333 \| 1150 \| 0.0001 \| - \|
	\| 40.0 \| 1200 \| 0.0001 \| - \|
	\| 41.6667 \| 1250 \| 0.0001 \| - \|
	\| 43.3333 \| 1300 \| 0.0001 \| - \|
	\| 45.0 \| 1350 \| 0.0 \| - \|
	\| 46.6667 \| 1400 \| 0.0 \| - \|
	\| 48.3333 \| 1450 \| 0.0001 \| - \|
	\| 50.0 \| 1500 \| 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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