master_cate_lh13 / 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: 네이쳐리빙 모던 트롤리 스윙 3단 빨래바구니 E) ★한정특가★_E05간편보관접이식대야(S)_블루 서전통상
	- text: 빨래방망이 다듬이방망이 2P세트 이불방 다듬잇방망이 신규A
	- text: '[홈앤하우스]라탄 패턴 사각 햄퍼 80L 내추럴/단품 패션플러스'
	- text: 빨래판 세면대 세라믹 매립형 가정용 발코니 세미빌트인 간이 개수대 4. A형 35x46 - 수전 별도 구매 고야글로벌
	- text: 전동빨래건조대 베란다 건조대 전동 자동 천정 천장형 천장 T-V917Yahei텔레스코픽로드 데일바이
	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: 0.9600729631130929
	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>'신발세척 브러쉬 싱크대 가벼운 다용도 브러시 실내화 푸른은하수마트'</li><li>'방글방글운동화솔5P 다용도솔 따뜻한마켓'</li><li>'신발닦이솔 아이디어 청소솔 화이트 에이치비상사'</li></ul> \|
	\| 8.0 \| <ul><li>'삼정 스마트세탁망 내복용 31x50cm 4441 장가요몰'</li><li>'무형광 국산 세탁망 맘스필 브라망 사각 원형 특대형 드럼세탁기 빨래망 사각XL-미세망 rodzina'</li><li>'고급형 세탁기 먼지망/이물질/먼지/세탁기 거름망 블루 레이어드컴퍼니'</li></ul> \|
	\| 1.0 \| <ul><li>'면마직류 고급 다리미풀 정전기방지 말표 말표다리미풀480ml 추가E'</li><li>'말표 다리미풀 다림질풀 뉴 스프레이 다리미용품 480ml 고급의류 다림풀 의류다림 MinSellAmount 종종걸음샵'</li><li>'키밍 핸드 스팀 다리미판 다리미 스폰지 장갑 상품선택_7403 삼각형 꼬부기 가게'</li></ul> \|
	\| 6.0 \| <ul><li>'다용도실 빨래판 세면대 속옷빨래 사각형 간이세면기 비 47x54 더드컨트리'</li><li>'[여름신상베스트]키높이빨래판 자스트데어(JUST THERE)'</li><li>'Per 빨래판 속 튼튼한 부드러운판 논슬립 손빨래 미끄럼방지 비누수납 일체형디자인 옷빨래 실용적인 대형_그레이 투베스트컴퍼니'</li></ul> \|
	\| 5.0 \| <ul><li>'집 소형 편리한 공간활용 미니행거-집게20p 접이식 폴딩 가정용 심플 스위트 가이 (sweet guy)'</li><li>'다용도 만능 신발정리 휴지집게 구두 물건 잡는 집게 미아앤미오 컴퍼니'</li><li>'접이식 미니행거-집게20p 폴딩 소형 공간활용 풍성한정원'</li></ul> \|
	\| 9.0 \| <ul><li>'세탁볼 실내건조세제 드럼 이용가능 매직클린 통돌이 행복나라'</li><li>'쇼핑추천 포함 양모볼 인기제품 건조기 키니툴 6P 파우치 핑쇼24'</li><li>'[1300K] 고슴도치 세탁볼 3개 세트 엔에이치엔위투 주식회사'</li></ul> \|
	\| 4.0 \| <ul><li>'키친아트 빨래 삶는 통 냄비 인덕션 빨래솥 삶숙이 32cm 3.키친아트 34cm(일반) 척척홀릭'</li><li>'삶순이 행주삶기 행주 냄비 스테인리스 삶기 22cm 빠른대행'</li><li>'키친아트 스팀빨래솥30CM ZW1E3205 윤지상회'</li></ul> \|
	\| 0.0 \| <ul><li>'일상드로우 다리미판 스팀다리미 스탠드 접이식 스팀다리미판 스탠드 다다마스'</li><li>'휴대용 핸드 스팀 다리미판 패드 장갑 핸드다리미판장갑 주식회사 아이니쥬'</li><li>'가담다 접이식 스팀 좌식 플립 다리미판 (스톤그레이/프리미엄블랙)/높이조절7단 다리미판 모음전 우마형 화이트 가담다공식스토어'</li></ul> \|
	\| 7.0 \| <ul><li>'캠핑용 빨래줄 여행용 빨랫줄 품 휴대용 용품 야외 건조 동그라미'</li><li>'스테인레스 빨래줄 이불 걸이대 스텐 빨랫줄 와이어 304 빨랫줄(10m) 버클세트 스마일_'</li><li>'빨래집게걸이 12P 빨레 집개 건조대 찝개 형 빨래 집게 연두 골드코스트'</li></ul> \|
	\| 11.0 \| <ul><li>'[판매순위1등] 삼성에어드레서 5벌 DF-FL 호환필터 팡스토리'</li><li>'부드러운 카카오프렌즈 기획전 차박 무릎 학생 잇템 캠핑 춘식이 사무실 담요 청춘유통'</li><li>'암앤해머 베이킹 소다 6.12kg x 2 세척 탈취 백두마켓'</li></ul> \|
	\| 3.0 \| <ul><li>'벽걸이 메쉬 빨래 바구니 벽걸이 메쉬빨래바구니(핑크) 위드주'</li><li>'친환경 플라스틱 라탄 대용량 특대형 빨래바구니 60L 2. 베이지 '</li><li>'빨래통 3단 트롤리 4단 이동식 스윙빨래바구니 세탁함 빨래바구니3단 사구있오'</li></ul> \|
	\| 2.0 \| <ul><li>'리빙나이스 천장건조대 스텐봉 200cm 소경환'</li><li>'삼덕기업 하드웰 PVC코팅봉 스텐봉 베란다 천장빨래건조대 스텐봉 표준형 까바짬'</li><li>'전동빨래건조대 발코니 베란다 천장형 조명 C 꾸대'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Metric \|
	\|:--------\|:-------\|
	\| all \| 0.9601 \|

	## 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_lh13")
	# Run inference
	preds = model("빨래방망이 다듬이방망이 2P세트 이불방 다듬잇방망이 신규A")
	```

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	### Downstream Use

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 3 \| 9.8017 \| 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 \| 0.4631 \| - \|
	\| 0.5319 \| 50 \| 0.4236 \| - \|
	\| 1.0638 \| 100 \| 0.2102 \| - \|
	\| 1.5957 \| 150 \| 0.114 \| - \|
	\| 2.1277 \| 200 \| 0.0733 \| - \|
	\| 2.6596 \| 250 \| 0.0541 \| - \|
	\| 3.1915 \| 300 \| 0.0316 \| - \|
	\| 3.7234 \| 350 \| 0.0104 \| - \|
	\| 4.2553 \| 400 \| 0.0098 \| - \|
	\| 4.7872 \| 450 \| 0.0039 \| - \|
	\| 5.3191 \| 500 \| 0.0026 \| - \|
	\| 5.8511 \| 550 \| 0.0002 \| - \|
	\| 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 \| 0.0 \| - \|
	\| 13.2979 \| 1250 \| 0.0001 \| - \|
	\| 13.8298 \| 1300 \| 0.0001 \| - \|
	\| 14.3617 \| 1350 \| 0.0 \| - \|
	\| 14.8936 \| 1400 \| 0.0001 \| - \|
	\| 15.4255 \| 1450 \| 0.0 \| - \|
	\| 15.9574 \| 1500 \| 0.0001 \| - \|
	\| 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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