master_cate_fd14 / 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: 네스프레소 버츄오 캡슐 머그 멜로지오 1Box (10캡슐) 아이스 라떼 03. 알티시오 제이유
	- text: 맥심 티오피 스위트 아메리카노 200ml (주)디에이치솔루션
	- text: 굿라이프365 스피아민트 삼각티백 50개입 익모초 삼각티백 50개입 주식회사 굿라이프365
	- text: 칠성사이다 제로 ECO 무라벨 300ml 20pet [음료] 커피음료_맥심티오피심플리스무스로스티라떼360mlx20개 옐로우로켓
	- text: 동서식품 kanu 미니 마일드 로스트 아메리카노 0.9g 카누디카페인 0.9g 100+20(120개입) 강유팩토리
	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: 0.6535632816801699
	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: 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 \|
	\|:------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 10.0 \| <ul><li>'라인바싸 탄산수 레몬 500ml 20개 자몽 500ml 20개 에이치앤제이원'</li><li>'라인바싸 탄산수 파인애플 500ml 20입 1박스 (추가)+ 플레인 1박스 동아오츠카주식회사'</li><li>'코카콜라 씨그램 레몬 350mlx24페트 탄산수모음 15_트레비 라임 355mlx24CAN 주식회사대성에프앤비'</li></ul> \|
	\| 8.0 \| <ul><li>'맥심 아이스 커피믹스 110T +커피믹스 스틱 2T 콤부차_다농원 콤부차 세븐베리 20T+보틀 주식회사 경일종합식품 케이마트몰'</li><li>'[카누]카누 디카페인 미니 0.9g x 120개입 - 1개 HN 다크 로스트 0.9g 100+텀블러(사은품) 하나엔피그먼트'</li><li>'프렌치카페 카페믹스 스테비아 디카페인 10.3g x 100개입 대은상사'</li></ul> \|
	\| 1.0 \| <ul><li>'매일유업 매일우유 매일두유 99.9 190ml 12개 12개 테켄종합상사'</li><li>'매일유업 마이너피겨스 유기농 오트밀크 1L 주식회사 지룩'</li><li>'아몬드 브리즈 뉴트리플러스 프로틴 190ml 48개 스타일바이맘'</li></ul> \|
	\| 6.0 \| <ul><li>'이제부터 무가당 무설탕 생강진액 생강차 생강즙 생강청 1L ★이벤트★ 3+1(생강청)-박스없음_소비자가 태후자연식품영농조합법인'</li><li>'티젠 콤부차 파인애플 5g x 30개입 샤인머스켓(30개입) 엠비알글로벌'</li><li>'[오설록](신세계 본점)세작 80 g(잎차) 주식회사 에스에스지닷컴'</li></ul> \|
	\| 5.0 \| <ul><li>'파낙스 참다음 매실 원액 1.5L/6배희석 로쏘 레몬음료 베이스 1L (주) 이카루스'</li><li>'동원 덴마크 푸르티 포도 주스 120mL x 24개 블라썸플라워'</li><li>'썬업 과일야채샐러드 그린 200ml x 24팩 과일야채 샐러드 레드 200ml x 24팩 하니컴퍼니'</li></ul> \|
	\| 9.0 \| <ul><li>'허쉬 코코아 가루 분말 226g W-00652_허쉬코코아파우더226g(파손) 월푸드'</li><li>'기라델리 프리미엄 핫코코아믹스 초콜렛 907g X 1박스(4개) 고고커피'</li><li>'Nestle Hot Cocoa 핫 코코아 믹스 30개 0.28온스 207799 무설탕 무지방_2개들이 팩 더블스토어'</li></ul> \|
	\| 4.0 \| <ul><li>'코카콜라 태양의 식후비법 W차 500ml (주)디에이치솔루션'</li><li>'광동 힘찬하루 헛개차 1.5L 1개 대패트_게토레이 레몬 1.5L 12개 대영상사'</li><li>'웰그린 스위츠 복숭아 녹차 음료 340ml 티트라 레몬그린티 제로 500mlX24PET 브론스코리아(주)'</li></ul> \|
	\| 0.0 \| <ul><li>'레드불 에너지 드링크 355ml (6개) 카페인 타우린 비타민 알프스 워터 대량 구매 노건'</li><li>'청정원 홍초 석류 1.5L 홍초 블루베리 1.5L (주) 이카루스'</li><li>'청정원 홍초 자몽 900ml 아이스티_티오 아이스티 레몬맛40T 주식회사 경일종합식품 케이마트몰'</li></ul> \|
	\| 7.0 \| <ul><li>'동서 티오 아이스티 복숭아 70T +커피믹스 스틱 2T 콤부차_다농원 콤부차 리치 20T+보틀 주식회사 경일종합식품 케이마트몰'</li><li>'립톤 아이스티 복숭아 770g 레몬 770g_자몽 아이스티 키트(2개입) 유니레버코리아 (주)'</li><li>'술픽 하이트진로 토닉워터 600ml 대용량 술벙커 주식회사 농업회사법인 이천지점'</li></ul> \|
	\| 11.0 \| <ul><li>'포모나 블루베리스무디 2kg 블루베리농축액 (주)제이제이푸드시스템'</li><li>'베오베 오곡 파우더 1kg 라떼 곡물 미숫가루 분말 티에이치커피 티에이치커피'</li><li>'타코 복숭아 아이스티 /선택 08.블루베리라떼870g 주식회사 커피바바'</li></ul> \|
	\| 3.0 \| <ul><li>'[매니저배송] MPRO 장&면역+피부 (5개입) (주)에치와이'</li><li>'요플레 닥터캡슐 베리믹스 130mLx4개/1000배/냉장무배 대명유통'</li><li>'매일바이오 알로에 120g 12개_냉장 매일유업 주식회사'</li></ul> \|
	\| 2.0 \| <ul><li>'화인바이오 지리산 물하나 2L X 6개 글로벌웨이브'</li><li>'하이트 천연광천수 미네랄 석수 무라벨 500ml 20pet ◇ 석수 무라벨 500ml 20pet 주식회사 부산종합유통'</li><li>'아이시스8.0 300ml x 1BOX(20PET) 생수 아이시스8.0 200ml(40p) (주)하나유통'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Metric \|
	\|:--------\|:-------\|
	\| all \| 0.6536 \|

	## 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_fd14")
	# Run inference
	preds = model("맥심 티오피 스위트 아메리카노 200ml (주)디에이치솔루션")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 4 \| 9.805 \| 20 \|

	\| 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 \|
	\| 10.0 \| 50 \|
	\| 11.0 \| 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0106 \| 1 \| 0.3763 \| - \|
	\| 0.5319 \| 50 \| 0.3216 \| - \|
	\| 1.0638 \| 100 \| 0.1166 \| - \|
	\| 1.5957 \| 150 \| 0.0863 \| - \|
	\| 2.1277 \| 200 \| 0.0548 \| - \|
	\| 2.6596 \| 250 \| 0.0559 \| - \|
	\| 3.1915 \| 300 \| 0.0323 \| - \|
	\| 3.7234 \| 350 \| 0.0301 \| - \|
	\| 4.2553 \| 400 \| 0.0191 \| - \|
	\| 4.7872 \| 450 \| 0.0127 \| - \|
	\| 5.3191 \| 500 \| 0.0059 \| - \|
	\| 5.8511 \| 550 \| 0.0003 \| - \|
	\| 6.3830 \| 600 \| 0.0002 \| - \|
	\| 6.9149 \| 650 \| 0.0001 \| - \|
	\| 7.4468 \| 700 \| 0.0001 \| - \|
	\| 7.9787 \| 750 \| 0.0001 \| - \|
	\| 8.5106 \| 800 \| 0.0001 \| - \|
	\| 9.0426 \| 850 \| 0.0001 \| - \|
	\| 9.5745 \| 900 \| 0.0001 \| - \|
	\| 10.1064 \| 950 \| 0.0001 \| - \|
	\| 10.6383 \| 1000 \| 0.0001 \| - \|
	\| 11.1702 \| 1050 \| 0.0001 \| - \|
	\| 11.7021 \| 1100 \| 0.0001 \| - \|
	\| 12.2340 \| 1150 \| 0.0001 \| - \|
	\| 12.7660 \| 1200 \| 0.0001 \| - \|
	\| 13.2979 \| 1250 \| 0.0 \| - \|
	\| 13.8298 \| 1300 \| 0.0001 \| - \|
	\| 14.3617 \| 1350 \| 0.0001 \| - \|
	\| 14.8936 \| 1400 \| 0.0001 \| - \|
	\| 15.4255 \| 1450 \| 0.0 \| - \|
	\| 15.9574 \| 1500 \| 0.0 \| - \|
	\| 16.4894 \| 1550 \| 0.0 \| - \|
	\| 17.0213 \| 1600 \| 0.0 \| - \|
	\| 17.5532 \| 1650 \| 0.0 \| - \|
	\| 18.0851 \| 1700 \| 0.0 \| - \|
	\| 18.6170 \| 1750 \| 0.0 \| - \|
	\| 19.1489 \| 1800 \| 0.0 \| - \|
	\| 19.6809 \| 1850 \| 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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