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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: 오너클랜 블랙헤드 클리닝 24개입 코팩 스킨애 대용량 참숯 옵션없음 모던벙커
	- text: 히든올가 히아루론산 마스크 400g 옵션없음 판타시아
	- text: '[셀러허브 1]프롬더스킨 글루타치온 콜라겐 팩 50g 1개 RS 기본 에스케이스토아주식회사'
	- text: 볼라욘 스피넴 파우더500g(모델링 마스크)+샘플+팩도구세트 옵션없음 수애
	- text: 피부관리실/피부 미용 실기 비타민 열석고 1kg 황토 석고 700g 주식회사 엔에프코리아
	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: 0.6112115732368897
	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: 8 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 \|
	\|:------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 1.0 \| <ul><li>'리쥬란 마스크팩 리쥬란힐러 마스크 2박스 총10개입 (병원용) MKSISTER'</li><li>'폴리머 수화겔 하이드로겔 마스크 팩세트 35gx4P 주식회사 에이에이앤티'</li><li>'바이오던스 바이오 콜라겐 리얼 딥 마스크 4매 콜라겐마스크팩 저분자 블망몰'</li></ul> \|
	\| 6.0 \| <ul><li>'블랙헤드 아웃 시트 35개입 3개 옵션없음 건강드림'</li><li>'CNP 차앤박 안티 포어 블렉헤드 클리어 키트 10매세트 옵션없음 구메구메'</li><li>'아이스 얼음팩 쿨링 마스크 피부 자극 붓기 열 진정 자외선 옵션없음 삼성메디원'</li></ul> \|
	\| 5.0 \| <ul><li>'이즈앤트리 머그워트 카밍 클레이 마스크 100ml 1개 옵션없음 비래유통'</li><li>'정품 정케이스 설화수 백삼팩 120ml 옵션없음 맥스베스트'</li><li>'마미케어 들깨 워시오프 팩 100g 옵션없음 주식회사 올리브인터내셔널'</li></ul> \|
	\| 4.0 \| <ul><li>'메노킨 30초 퀵 버블 마스크_브라이트 옵션없음 주식회사 포레스트에비뉴'</li><li>'아비브 라이스 프로바이오틱스 오버나이트 마스크 배리어 젤리 80ml 옵션없음 주식회사 지에스원(GS ONE CO.,LTD.)'</li><li>'[갤러리아] [GL]슈퍼 나이츠 - 클리어 수딩 마스크 점보 120ml(한화갤러리아㈜ 센터시티) 옵션없음 한화갤러리아(주)'</li></ul> \|
	\| 7.0 \| <ul><li>'끌레드벨 원킬 브이 리프팅 마스크 옵션없음 아이앤에이 (INA)'</li><li>'루미너스 쿨링팩 4개 쿨링팩 4개 히얼위아'</li><li>'메디필 콜라겐 마스크 2+1 옵션없음 성원아토'</li></ul> \|
	\| 2.0 \| <ul><li>'골프 피부진정 패치 5매 아이패치 하이드로겔 등산 옵션없음 아이템코리아주식회사(Item KOREA Inc.)'</li><li>'13 에어뮤즈 멜라이드 패치스탠다드 아이패치 야외 5매 옵션없음 네이비몰'</li><li>'퍼스트랩 프로바이오틱 마스크 시즌4 25g 1매 옵션없음 다사다 유한책임회사'</li></ul> \|
	\| 3.0 \| <ul><li>'볼라욘 스피넴 파우더 스피루리나 모델링팩 500g+팩도구+샘플 옵션없음 라플레르'</li><li>'데쌍브르 골드 필 오프 모델링팩 1kg 데쌍브르 골드 필 오프 모델링팩 1kg 마가렛스킨'</li><li>'데쌍브르 골드 모델링 마스크 1000g + 고무볼,스파츌라,앰플 골드 모델링마스크 반하다'</li></ul> \|
	\| 0.0 \| <ul><li>'IS-SA 오이 바이탈라이징 마사지크림 500g 옵션없음 우리의쇼핑'</li><li>'MSM글루코사민크림 핫 120ml 맛사지크림 뉴나노웰제약 옵션없음 운호'</li><li>'더후 공진향 인양 넥앤페이스 탄력 리페어75ml 옵션없음 씨플랩'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.6112 \|

	## 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_bt2_test")
	# Run inference
	preds = model("히든올가 히아루론산 마스크 400g 옵션없음 판타시아")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 3 \| 9.7453 \| 23 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0.0 \| 16 \|
	\| 1.0 \| 10 \|
	\| 2.0 \| 42 \|
	\| 3.0 \| 21 \|
	\| 4.0 \| 20 \|
	\| 5.0 \| 19 \|
	\| 6.0 \| 15 \|
	\| 7.0 \| 18 \|

	### 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0526 \| 1 \| 0.4875 \| - \|
	\| 2.6316 \| 50 \| 0.3322 \| - \|
	\| 5.2632 \| 100 \| 0.0442 \| - \|
	\| 7.8947 \| 150 \| 0.0025 \| - \|
	\| 10.5263 \| 200 \| 0.0002 \| - \|
	\| 13.1579 \| 250 \| 0.0001 \| - \|
	\| 15.7895 \| 300 \| 0.0001 \| - \|
	\| 18.4211 \| 350 \| 0.0001 \| - \|
	\| 21.0526 \| 400 \| 0.0001 \| - \|
	\| 23.6842 \| 450 \| 0.0001 \| - \|
	\| 26.3158 \| 500 \| 0.0001 \| - \|
	\| 28.9474 \| 550 \| 0.0001 \| - \|
	\| 31.5789 \| 600 \| 0.0001 \| - \|
	\| 34.2105 \| 650 \| 0.0001 \| - \|
	\| 36.8421 \| 700 \| 0.0001 \| - \|
	\| 39.4737 \| 750 \| 0.0001 \| - \|
	\| 42.1053 \| 800 \| 0.0001 \| - \|
	\| 44.7368 \| 850 \| 0.0001 \| - \|
	\| 47.3684 \| 900 \| 0.0001 \| - \|
	\| 50.0 \| 950 \| 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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