master_cate_lh12 / 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: 이글루캠 S3플러스 2K 300만화소 가정용 CCTV 홈 카메라 홈캠 (주) 트루엔
	- text: 멕시코 조트비누 400g 만능세제 세탁 세제 빨래 기름때 얼룩제거 욕실청소 라코로나 조트비누 400g (블루) 리아앤리브
	- text: 피플연구소 양면방수 매트 돗자리 145x150cm 로지브라운 피크닉 감성 화이트_M 스트림프러덕
	- text: 다우니 울트라 에이프릴 프레시 5.03L [생활] 섬유유연제_피죤 핑크로즈 3.1L x 4개 옐로우로켓
	- text: 창문 자동 롤방충망 상하식 미세 대형 셀프교체 사면 가로300x세로250mm 사면_가로1600mm(1501~1600)_세로600mm(501~600)
	NK테크
	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: 0.7296620438939007
	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>'선반형 스텐 점보롤 디스펜서 폰 거치 케이스 유광실버 04_CNDH-03 스텐점보 유광 골드선반 (주)엘에스트레이드'</li><li>'하이브리드 쓰리킹 미트페이퍼 해동지 2롤 선택04.크록스 위생멸균 흡수지 2롤 찐텐마켓'</li><li>'크리넥스 클린케어 아쿠아 메가롤 3겹 50m 30롤 클린소프트 3겹 데코 30m 30롤 메리앤'</li></ul> \|
	\| 2.0 \| <ul><li>'애경 로얄 프로폴리스 에디션 선물세트 추석 선물세트 명절선물세트 샴푸 린스 강성수'</li><li>'로얄 프로폴리스 셀렉션 29호 X 1개 고기능 에디션으로 행복 선물 MinSellAmount 현둘마마'</li><li>'대림바스 카카오 라이언 선물세트 GIFT BOX (샤워기+샤워줄+필터4P+염소제거볼1P) 카카오 선물세트 GIFT BOX [라이언] 바스템'</li></ul> \|
	\| 0.0 \| <ul><li>'초소형 카메라 CCTV 무선 미니 감시 초소형 카메라 + 128GB SD카드_(리뷰약속)SD카드 32GB+방수케이스+거치대2종 일레닉'</li><li>'지르콘 멀티 탐지기 HD70 멀티탐색기 ZIRCON 멀티탐지 프랑스달'</li><li>'메모리선택 티피링크 Tapo TC70 200만화소 360도회전 실내무선카메라 홈CCTV 야간흑백전환 선택4 Tapo TC70+메모리카드128G 삼성디앤씨주식회사'</li></ul> \|
	\| 4.0 \| <ul><li>'바운스 건조기 드라이시트 아웃도어후레쉬 160매 1개 에너저틱'</li><li>'피죤 핑크로즈 3.1L 피죤 비앙카 3100ml 1입 주식회사 드림쇼핑'</li><li>'다우니 엑스퍼트 실내건조 섬유유연제 1L 생화향기 코튼퓨어 용기 1L (주)모던컴퍼니'</li></ul> \|
	\| 8.0 \| <ul><li>'엔젤가드 특허 90도 회전 전기모기채 충전식전자파리채 건전지대 01. 특허받은 led회전모기채(충전식 대) 핑크 WOOD파크'</li><li>'ODF169432해피홈 에어넷 걸이형 제이엘 코리아(JL KOREA)'</li><li>'초강력해충킬러전기모기채(특대) 비트테크노'</li></ul> \|
	\| 6.0 \| <ul><li>'헨켈 퍼실 파워젤 라벤더 드럼용 리필 1.8L 퍼실 파워젤 드럼용 1.8L(일반/드럼 겸용) 누리플러스'</li><li>'다우니 프리미엄 엑스퍼트 실내건조 세탁세제 액체형 1.9L 08_다우니 코튼 퓨어러브 1L (주)넥스트월드코퍼레이션'</li><li>'애경산업 스파크 찬물에 잘녹는 세탁세제 리필 9.5kg 1개 쇼킹(SHOW KING)'</li></ul> \|
	\| 3.0 \| <ul><li>'쇼핑카트 바퀴달린장바구니 시장바구니캐리어 접이식 손수레 핸드 카트 마트 베이지체크패턴 (타입07) 소형 패턴 8종_체크 곤색 에이오더스(A Orders)'</li><li>'스테인레스 가정용 소형 원형 스퀘어 스탠드 방지 재 01.락 구형 블랙 라지 에이미어블'</li><li>'1초완성 원터치모기장 텐트 침대 사각 아기 대형 창문 2_베이직 블루 2~3인용(200X150) 다샵몰'</li></ul> \|
	\| 5.0 \| <ul><li>'금비 겉기저귀 프리미엄 와이드매직 실속형 대형 10p+10p(총 2팩) 팬티기저귀 대형 10p+10p 나루(NARU)리테일'</li><li>'디펜드 스타일 언더웨어 슬림 라이트핏 중형 여성용 10개입x8팩/요실금팬티 성인기저귀 송광물류'</li><li>'유한킴벌리 디펜드 안심플러스 중형 9매 -1개 주식회사 민영'</li></ul> \|
	\| 7.0 \| <ul><li>'말표 신발 탈취제 100ml 발냄새 신발냄새 제거 MinSellAmount 대코아'</li><li>'슈즈쿨 빨강색 신발건조탈취제 냄새 습기제거 MinSellAmount SMH만물상회'</li><li>'페브리즈 포맨 쿨아쿠아향 리필 320ml 포맨 쿨아쿠아향 리필 320ml 지기샵'</li></ul> \|
	\| 1.0 \| <ul><li>'좋은느낌 입는 오버나이트 중형 8매 x 1팩 주식회사 다올연구소'</li><li>'닉스컵 내몸을 생각하는 안전한 실리콘 생리컵 소형 luckytiger3'</li><li>'화이트 수퍼흡수 중형 (30+6)개입 (주) 삼성 에이치엔씨'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Metric \|
	\|:--------\|:-------\|
	\| all \| 0.7297 \|

	## 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_lh12")
	# Run inference
	preds = model("이글루캠 S3플러스 2K 300만화소 가정용 CCTV 홈 카메라 홈캠 (주) 트루엔")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 3 \| 9.964 \| 24 \|

	\| 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 \| 0.3941 \| - \|
	\| 0.6329 \| 50 \| 0.3041 \| - \|
	\| 1.2658 \| 100 \| 0.1323 \| - \|
	\| 1.8987 \| 150 \| 0.0705 \| - \|
	\| 2.5316 \| 200 \| 0.0185 \| - \|
	\| 3.1646 \| 250 \| 0.021 \| - \|
	\| 3.7975 \| 300 \| 0.0292 \| - \|
	\| 4.4304 \| 350 \| 0.0158 \| - \|
	\| 5.0633 \| 400 \| 0.0176 \| - \|
	\| 5.6962 \| 450 \| 0.0001 \| - \|
	\| 6.3291 \| 500 \| 0.0079 \| - \|
	\| 6.9620 \| 550 \| 0.0004 \| - \|
	\| 7.5949 \| 600 \| 0.0001 \| - \|
	\| 8.2278 \| 650 \| 0.0001 \| - \|
	\| 8.8608 \| 700 \| 0.0001 \| - \|
	\| 9.4937 \| 750 \| 0.0001 \| - \|
	\| 10.1266 \| 800 \| 0.0001 \| - \|
	\| 10.7595 \| 850 \| 0.0001 \| - \|
	\| 11.3924 \| 900 \| 0.0001 \| - \|
	\| 12.0253 \| 950 \| 0.0001 \| - \|
	\| 12.6582 \| 1000 \| 0.0 \| - \|
	\| 13.2911 \| 1050 \| 0.0 \| - \|
	\| 13.9241 \| 1100 \| 0.0001 \| - \|
	\| 14.5570 \| 1150 \| 0.0 \| - \|
	\| 15.1899 \| 1200 \| 0.0 \| - \|
	\| 15.8228 \| 1250 \| 0.0 \| - \|
	\| 16.4557 \| 1300 \| 0.0001 \| - \|
	\| 17.0886 \| 1350 \| 0.0 \| - \|
	\| 17.7215 \| 1400 \| 0.0 \| - \|
	\| 18.3544 \| 1450 \| 0.0 \| - \|
	\| 18.9873 \| 1500 \| 0.0 \| - \|
	\| 19.6203 \| 1550 \| 0.0001 \| - \|

	### 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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