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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: 니베아 맨 프레시 킥 쉐이빙 폼 200ml 옵션없음 세계로마트
	- text: '[백화점즉시입고/ ] 크리니크 포 맨 오일 컨트롤 엑스폴리에이팅 토닉 200ml 크리니크 옴므 옵션없음 제이앤케이'
	- text: 디오프러스 AC시카 카밍 부스팅 트라이얼키트 4종 세트 1세트 옵션없음 건강드림
	- text: '[더페이스샵] 더프레시포맨 수분플루이드 200 mL 옵션없음 (주)엘지생활건강'
	- text: 랩시리즈 올인원 멀티 액션 훼이스 워시 100ml 11203597 올인원 멀티 액션 훼이스 워시 100ml 세론세론
	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: 0.6473429951690821
	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: 13 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 \|
	\|:------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 13.0 \| <ul><li>'엠도씨 릴리프 폼클렌징 150g 옵션없음 라봉'</li><li>'홀츠포맨 남자클렌징폼 500ml 옵션없음 (주)안나홀츠'</li><li>'[당일출고] 랩시리즈 올인원 멀티 액션 훼이스 워시 100ml 옵션없음 제이에이치컴퍼니'</li></ul> \|
	\| 12.0 \| <ul><li>'홀츠포맨 옴므 남성 남자 화장품 주름개선 보습 영양크림 100ml 홀츠포맨'</li><li>'정품 위너크림파워 특허받은남성크림벌침 옵션없음 큐샵몰'</li><li>'엠도씨 화이트닝 톤 업 크림 50ml 엠도씨'</li></ul> \|
	\| 6.0 \| <ul><li>'[용량 +25%] 니베아 맨 프로텍트 앤 케어 쉐이빙폼 면도크림 250ml X 3개 센서티브 쉐이빙 폼 200ml X 3개 (주)에스티아이그룹'</li><li>'쉐이브젤 면도 폼 크림 영국 킹오브쉐이브 센서티브 175ml 04 센서티브 쉐이브 세럼 50ml 크리스탈링크'</li><li>'쉐이빙폼 저자극 면도크림 200ml 3개+3중날 면도기 3개 [BB3] 오리지널2개+센서티브2개 에스세이지 주식회사'</li></ul> \|
	\| 0.0 \| <ul><li>'사타구니 고환 가려움 곰팡이균 습진 백선 완선 연고 옵션없음 글로리아'</li><li>'사타구니 땀냄새케어 스프레이휴대 남성청결제 세정제 옵션없음 담고 마켓'</li><li>'서라봉스프레이 옵션없음 주식회사 햇살메디컬'</li></ul> \|
	\| 5.0 \| <ul><li>'설화수 맨 본윤 남자 선크림 썬크림 선물 옴므 올인원 비비 톤업 설화수본윤'</li><li>'달바 비건 맨즈 쿨링 밸런싱 선크림 50ml(SPF50+) 달바 공식스토어'</li><li>'랩시리즈 파워 프로텍터 100ml SPF50 옵션없음 메가랜드'</li></ul> \|
	\| 2.0 \| <ul><li>'392755 NEW로맨틱 에멀전 130ml 옵션없음 제이에프무역'</li><li>'[LG생활건강]피지오겔 DMT 포맨 플루이드100ml x 1개 옵션없음 에스케이스토아주식회사'</li><li>'보닌 더 스타일 에멀전 135ml 더 스타일 에멀전 135ml 보닌 옵션없음 우리유통상사'</li></ul> \|
	\| 3.0 \| <ul><li>'얼음 아이스 남성 데일리 얼굴팩 마스크팩 시트 Gosun'</li><li>'퓨어덤 릴랙스 하이드라 남성용 마스크 1매 퓨어덤'</li><li>'릴리프 맨테라피 마스크 18ml x4 엠도씨코리아'</li></ul> \|
	\| 8.0 \| <ul><li>'[당일출고] 보닌 더 캐릭터 블랙 시그니처 토너 140ml 옵션없음 현영'</li><li>'비오템 옴므 아쿠아파워 토너 200ml/남성스킨 젤타입/ 국내발송 정품 리퍼브상품(새상품이나 용기 스티커 들뜸) 주식회사 오즈비엔에이치'</li><li>'크리니크 포 맨 엑스폴리에이팅 토닉 200ml 옵션없음 이엘씨에이한국 (유)'</li></ul> \|
	\| 10.0 \| <ul><li>'크리니크 포 맨 맥시멈 하이드레이터 액티베이티드 워터-젤 컨센트레이트 48ml 옵션없음 에이치코리아'</li><li>'설화수 본윤 에센스 140ml 유통기한 27년 설화수'</li><li>'MISSHA 맨즈 큐어 앰플 에센스 150ml 미샤'</li></ul> \|
	\| 9.0 \| <ul><li>'클라랑스 맨 에너자이징 아이 젤 15ml 클라랑스'</li><li>'랩시리즈 데일리 레스큐 에너자이징 아이 트리트먼트 15ml 랩시리즈'</li><li>'셀맨 셀울트라 아이 세럼 엑스티 15ml 셀맨'</li></ul> \|
	\| 11.0 \| <ul><li>'우르오스 스킨 밀크 중건성 200ml 옵션없음 유니온'</li><li>'아이디얼포맨 퍼펙트올인원밀크 150ml + 100ml 남성용 아이디얼포맨 퍼펙트올인원밀크 150ml + 1 쇼핑천국이야기'</li><li>'BRTC 파워 옴므 올인원 솔루션 200ml 동의 린앤몰'</li></ul> \|
	\| 1.0 \| <ul><li>'꽃을든남자 이모션 스페셜세트(로션2p+스킨1p) 옵션없음 테디코스'</li><li>'(백화점 상품) 레드옥스남성 스페셜 2종 (W2B7B02) 1929235 기본상품 머스트겟'</li><li>'보닌 더 캐릭터 2종 기획세트 옵션없음 디제이커머스(DJ커머스)'</li></ul> \|
	\| 4.0 \| <ul><li>'랩시리즈 인스턴트 픽스 BB 틴티트 50ml 랩시리즈'</li><li>'오브제 매직 타투 브로우 남자 눈썹 정리 그리기 옵션없음 포리프'</li><li>'미프 미남크림 남자 썬 비비 커버 크림 50ml 미백 BB 잡티 커버 자외선 차단 미프'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.6473 \|

	## 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_bt0_test")
	# Run inference
	preds = model("니베아 맨 프레시 킥 쉐이빙 폼 200ml 옵션없음 세계로마트")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 4 \| 8.8913 \| 19 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0.0 \| 12 \|
	\| 1.0 \| 25 \|
	\| 2.0 \| 20 \|
	\| 3.0 \| 19 \|
	\| 4.0 \| 17 \|
	\| 5.0 \| 18 \|
	\| 6.0 \| 22 \|
	\| 8.0 \| 19 \|
	\| 9.0 \| 10 \|
	\| 10.0 \| 11 \|
	\| 11.0 \| 22 \|
	\| 12.0 \| 18 \|
	\| 13.0 \| 17 \|

	### 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0370 \| 1 \| 0.4919 \| - \|
	\| 1.8519 \| 50 \| 0.3712 \| - \|
	\| 3.7037 \| 100 \| 0.0915 \| - \|
	\| 5.5556 \| 150 \| 0.0351 \| - \|
	\| 7.4074 \| 200 \| 0.0122 \| - \|
	\| 9.2593 \| 250 \| 0.007 \| - \|
	\| 11.1111 \| 300 \| 0.0006 \| - \|
	\| 12.9630 \| 350 \| 0.0002 \| - \|
	\| 14.8148 \| 400 \| 0.0002 \| - \|
	\| 16.6667 \| 450 \| 0.0002 \| - \|
	\| 18.5185 \| 500 \| 0.0001 \| - \|
	\| 20.3704 \| 550 \| 0.0001 \| - \|
	\| 22.2222 \| 600 \| 0.0001 \| - \|
	\| 24.0741 \| 650 \| 0.0001 \| - \|
	\| 25.9259 \| 700 \| 0.0001 \| - \|
	\| 27.7778 \| 750 \| 0.0001 \| - \|
	\| 29.6296 \| 800 \| 0.0001 \| - \|
	\| 31.4815 \| 850 \| 0.0001 \| - \|
	\| 33.3333 \| 900 \| 0.0001 \| - \|
	\| 35.1852 \| 950 \| 0.0001 \| - \|
	\| 37.0370 \| 1000 \| 0.0001 \| - \|
	\| 38.8889 \| 1050 \| 0.0001 \| - \|
	\| 40.7407 \| 1100 \| 0.0001 \| - \|
	\| 42.5926 \| 1150 \| 0.0001 \| - \|
	\| 44.4444 \| 1200 \| 0.0001 \| - \|
	\| 46.2963 \| 1250 \| 0.0001 \| - \|
	\| 48.1481 \| 1300 \| 0.0001 \| - \|
	\| 50.0 \| 1350 \| 0.0001 \| - \|

	### 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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