master_cate_lh4 / README.md

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	---
	base_model: mini1013/master_domain
	library_name: setfit
	metrics:
	- metric
	pipeline_tag: text-classification
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: 동아제약 가그린 오리지널 가글 750ml (1개) 가그린 오리지널 820ml L스토어
	- text: 스켈링 입냄새 스케일러 치석제거기 구강청결기 치아 별이 빛나는 하늘 보라색 사치(sachi)
	- text: 텅브러쉬 4개세트 혀클리너 입냄새제거 혀백태제거 혀칫솔 i MinSellAmount 펀키보이
	- text: '[갤러리아] 폴리덴트 의치 부착재 민트향 70g x5개 한화갤러리아(주)'
	- text: 애터미 치약 프로폴리스 200g 입냄새 제거 미백 콜마 플렉스세븐
	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: metric
	value: 0.9477272727272728
	name: Metric
	---

	# 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: 10 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 \|
	\|:------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 9.0 \| <ul><li>'롤리팝 에디슨 항균 혀클리너 4종 퍼플 파랑새랑'</li><li>'텅브러쉬 혀클리너 입냄새제거 백태제거 혀칫솔 MinSellAmount 펀키보이'</li><li>'[생활도감] 혀클리너 세트 그린2개+네이비2개 주식회사 생활도감'</li></ul> \|
	\| 2.0 \| <ul><li>'셀프 가정용 스테인레스 스케일링 치석제거기 청소 도구 304 핑크 6종 세트 주식회사 클라우드'</li><li>'도구 치경 제거 편도석 제거기 입똥 편도결석 목똥 셀프 발광 귀걸이x수납함 로얄산티아고'</li><li>'소형 구취 측정기 테스트기 휴대용 냄새 악취 호흡 구강 입냄새측정기 자가진단 자가 가스 표준모델 _ 검정 행복초지'</li></ul> \|
	\| 0.0 \| <ul><li>'존슨앤존슨 구강청결 리스테린 쿨민트 250ml 후레쉬버스트 250ml - 1개 디아크코리아'</li><li>'일회용 여행용 가그린 라임10g 1개 휴대용 오리지널 가글스틱 오리지널 1개 예그린스페이스'</li><li>'가그린 제로 1200ML 쓱1day배송'</li></ul> \|
	\| 4.0 \| <ul><li>'투스노트 화이트닝겔 하루 2번 30분 투자로 누런이를 하얗게 투스노트 화이트닝겔 2주분 주식회사 네이처폴'</li><li>'루치펠로 미스틱포레스트 치약 180g 5개 원라이브팩토리'</li><li>'대형 치아모형 치아 모델 구조 인체 구강 치과 C. 구강 2배 확대(하아 제거 가능) 마켓 스페이스토끼'</li></ul> \|
	\| 8.0 \| <ul><li>'미소덴탈 교정장치보관함 교정기케이스 교정기통 교정기보관함-옐로우 (주)톡톡그린'</li><li>'성심 덴트크린 틀니세정제 36개입 2개 교정기 세척 희망메디'</li><li>'폴리덴트 맥스 씰 의치 부착재(의치 접착제) 70gx5개+샘플 1개 더마켓'</li></ul> \|
	\| 6.0 \| <ul><li>'백선생 왕타칫솔 베이직 스톤 10P 왕타'</li><li>'켄트칫솔 클래식 6개입 부드러운 칫솔 미세모 치아관리 어금니 치과칫솔 켄트 클래식 6개_켄트 탄 초극세모 1개(랜덤)_치간칫솔 8개입 1세트(레드 0.7mm) (주)지로인터내셔널'</li><li>'쿤달 딥 클린 탄력 항균 이중미세모 칫솔 부드러운모, 16입, 1개 구분 : 부드러운모 슈팅배송'</li></ul> \|
	\| 3.0 \| <ul><li>'오랄비 P&G 왁스치실 민트향 50m 01.왁스 치실 민트향 50m TH상사'</li><li>'오랄비 C자형 일회용 치실 30개입 1팩 NEW)치실C자 30개입[O121] 한국피앤지판매유한회사'</li><li>'오랄비 왁스치실 (50m 1개) 민트 디엔지유통'</li></ul> \|
	\| 5.0 \| <ul><li>'LG생활건강 죽염 명약원 골든프로폴리스 치약 플러스 120g MinSellAmount 오늘도연구소'</li><li>'엘지생활건강 죽염 잇몸고 치약 120g 1개 유니스'</li><li>'센소다인 오리지널 플러스 치약 100g 1개 dm 다임커머스'</li></ul> \|
	\| 7.0 \| <ul><li>'[유한양행]닥터버들 치약+칫솔 여행용세트 6개 신세계몰'</li><li>'[유한양행]닥터버들 휴대용 칫솔치약세트 1개 신세계몰'</li><li>'투톤 휴대용 칫솔 치약 케이스 캡슐형 답례품 투톤용 칫솔통 보관함 홀더 칫솔캡 캡슐칫 화이트블루 쏭리빙'</li></ul> \|
	\| 1.0 \| <ul><li>'일제 형상기억 마우스피스 아리더샾'</li><li>'혀용 코골이 방지 용품 대책용 마우스피스 8 개 세트 이와이리테일(EY리테일)'</li><li>'이갈이방지 치아 앞니 보호 유지 셀프 마우스피스 교정 2단계 코스모스'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Metric \|
	\|:--------\|:-------\|
	\| all \| 0.9477 \|

	## 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_lh4")
	# Run inference
	preds = model("애터미 치약 프로폴리스 200g 입냄새 제거 미백 콜마 플렉스세븐")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 3 \| 10.026 \| 23 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0.0 \| 50 \|
	\| 1.0 \| 50 \|
	\| 2.0 \| 50 \|
	\| 3.0 \| 50 \|
	\| 4.0 \| 50 \|
	\| 5.0 \| 50 \|
	\| 6.0 \| 50 \|
	\| 7.0 \| 50 \|
	\| 8.0 \| 50 \|
	\| 9.0 \| 50 \|

	### Training Hyperparameters
	- batch_size: (512, 512)
	- num_epochs: (20, 20)
	- max_steps: -1
	- sampling_strategy: oversampling
	- num_iterations: 40
	- body_learning_rate: (2e-05, 2e-05)
	- head_learning_rate: 2e-05
	- loss: CosineSimilarityLoss
	- distance_metric: cosine_distance
	- margin: 0.25
	- end_to_end: False
	- use_amp: False
	- warmup_proportion: 0.1
	- seed: 42
	- eval_max_steps: -1
	- load_best_model_at_end: False

	### Training Results
	\| Epoch \| Step \| Training Loss \| Validation Loss \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0127 \| 1 \| 0.4686 \| - \|
	\| 0.6329 \| 50 \| 0.2751 \| - \|
	\| 1.2658 \| 100 \| 0.1179 \| - \|
	\| 1.8987 \| 150 \| 0.0739 \| - \|
	\| 2.5316 \| 200 \| 0.0687 \| - \|
	\| 3.1646 \| 250 \| 0.0466 \| - \|
	\| 3.7975 \| 300 \| 0.0591 \| - \|
	\| 4.4304 \| 350 \| 0.0232 \| - \|
	\| 5.0633 \| 400 \| 0.0125 \| - \|
	\| 5.6962 \| 450 \| 0.0134 \| - \|
	\| 6.3291 \| 500 \| 0.0152 \| - \|
	\| 6.9620 \| 550 \| 0.0175 \| - \|
	\| 7.5949 \| 600 \| 0.0118 \| - \|
	\| 8.2278 \| 650 \| 0.007 \| - \|
	\| 8.8608 \| 700 \| 0.0003 \| - \|
	\| 9.4937 \| 750 \| 0.0002 \| - \|
	\| 10.1266 \| 800 \| 0.0001 \| - \|
	\| 10.7595 \| 850 \| 0.0001 \| - \|
	\| 11.3924 \| 900 \| 0.0001 \| - \|
	\| 12.0253 \| 950 \| 0.0001 \| - \|
	\| 12.6582 \| 1000 \| 0.0001 \| - \|
	\| 13.2911 \| 1050 \| 0.0001 \| - \|
	\| 13.9241 \| 1100 \| 0.0001 \| - \|
	\| 14.5570 \| 1150 \| 0.0001 \| - \|
	\| 15.1899 \| 1200 \| 0.0001 \| - \|
	\| 15.8228 \| 1250 \| 0.0001 \| - \|
	\| 16.4557 \| 1300 \| 0.0001 \| - \|
	\| 17.0886 \| 1350 \| 0.0001 \| - \|
	\| 17.7215 \| 1400 \| 0.0001 \| - \|
	\| 18.3544 \| 1450 \| 0.0001 \| - \|
	\| 18.9873 \| 1500 \| 0.0 \| - \|
	\| 19.6203 \| 1550 \| 0.0 \| - \|

	### Framework Versions
	- Python: 3.10.12
	- SetFit: 1.1.0.dev0
	- Sentence Transformers: 3.1.1
	- Transformers: 4.46.1
	- PyTorch: 2.4.0+cu121
	- Datasets: 2.20.0
	- Tokenizers: 0.20.0

	## 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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