master_cate_lh3 / 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: 브라인 쉬림프 뜰채, 소/중/대 색상랜덤, 고운망 물벼룩 치어 뜰재, 중 배움발전소
	- text: 30x45x45 (6T) - 30배럭 어항 수조 스마트어항
	- text: st1 협신 울트라 수중모터 50W UP-500ㄴ한강수족관 한강아쿠아 한라펫 관상어용품 수족관용품 펌프 모터 여 빙고라이프
	- text: 아마존 스테인레스 히터 200W 알에이디 주식회사
	- text: 네오 부스터 플랜츠 300ml 수초 액체 비료 어항 수초용 영양제 알에이디 주식회사
	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: 0.9219277108433735
	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 \|
	\|:------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 10.0 \| <ul><li>'OKE-6710hc 6428hc 사계절 온도 조절기 히터 냉각 전문가용 자동온도조절기 가변겸용 6710HC 태풍의눈'</li><li>'OKE-6710hc 6428hc 사계절 온도 조절기 히터 냉각 전문가용 자동온도조절기 사계절 6428HC 태풍의눈'</li><li>'페리하 HC 일반 히터 300W 파이브오션(Five Ocean)'</li></ul> \|
	\| 2.0 \| <ul><li>'(1+2) 흑사 바닥재 B3 0.8~2.2mm 2kg, BLACK, 1개 3set 알라이'</li><li>'편석 판석 1kg 어항돌 수조 조경석 철평석 레이아웃 [소] 중(1kg) 미니분경'</li><li>'칸후 베타은신처 베타침대 엠앤엠 주식회사'</li></ul> \|
	\| 0.0 \| <ul><li>'세드라 수륙양용펌프 KSP-120S 횟집 양식장 엠피엠'</li><li>'스핀들모터 고정밀 조각기 밀링 머신 조각 드릴링 머신 750 외풍로 스핀들모터 220V-L 스마트 빌리지'</li><li>'전동 실린더 엑추레이터 스트로크 컨트롤러 12V 24V 푸시로드 모터 스트로크 50MM_10MM/S 토크 3000N_24VDC 글로버리'</li></ul> \|
	\| 4.0 \| <ul><li>'수족관 다이 어항 받침대 선반 철제 우드 사이즈맞춤 60x30x70 기리스토어'</li><li>'반조립형 철재 축양장 2단 120x45 - 4자 광폭 수조 어항받침대 블랙_2단 기본형 스마트어항'</li><li>'레고 어항 마리모 베타 구피 물고기 키우기 수초 수조 용품 치어통 미니 수족관 블록 어항-조명_2-8. 어항 투명2 L 주식회사 대성상사'</li></ul> \|
	\| 9.0 \| <ul><li>'A+거북이 할로겐 조명소켓 국민오피스'</li><li>'리글라스 LED 등커버 LE-200 블랙 커버 자연과사람'</li><li>'파이시즈 PZ5-450B LED조명 주식회사 그루터기'</li></ul> \|
	\| 6.0 \| <ul><li>'네오 이탄세트 프리미엄 저압CO2 리필1회분 / 3회분 네오 이탄세트 프리미엄 좋은 사람들'</li><li>'네오 이탄세트 프리미엄 저압CO2 리필1회분 / 3회분 네오 이탄리필 1회분 좋은 사람들'</li><li>'일자연결 스타릿컴퍼니(Starlit Co.)'</li></ul> \|
	\| 3.0 \| <ul><li>'API 터틀픽스 118ml 거북이질병예방제 아쿠아 모모'</li><li>'켈란 산호&&치어 피딩용 스포이드 30cm / 총 39cm[K-072] 아마존수족관365'</li><li>'국제피쉬약품 골든 엘바진 7g 1개 어병 종합치료제 백점병 곰팡이병 아가미병 꼬리녹음병 아쿠아메이드'</li></ul> \|
	\| 5.0 \| <ul><li>'네오 플랜츠 Tab 70g 수초비료 수초어항 고체비료 네오 플랜츠 Fe 좋은 사람들'</li><li>'부세파란드라sp. 랜덤부세 6촉 활착용 핑크네 물방'</li><li>'포트 수초 전용 원형 토분 1개 신바람잡화점'</li></ul> \|
	\| 7.0 \| <ul><li>'그로비타 막대여과재 16X3.5X3.5cm 섬프 상면 대형여과제 그로비타 막대여과재 2개 유니온스토어'</li><li>'에하임 클래식 250 (2213) / 어항 여과기 부산기구'</li><li>'BASA바사 스펀지여과기 쌍기 그레이 (20년만에 개발된 신제품) 오쿠아(Oqua)'</li></ul> \|
	\| 1.0 \| <ul><li>'테트라 렙토민 에너지 250ml / 수생 반수생 거북이사료, 거북이 먹이 밥 물멍'</li><li>'그로비타(grovita) 플레코, 안시 전용사료 115g/250ml 물멍아쿠아'</li><li>'[택배비 2500] 러브라바 200g 6호 양어장종묘사료 6호 200g 아쿠아시티'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Metric \|
	\|:--------\|:-------\|
	\| all \| 0.9219 \|

	## 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_lh3")
	# Run inference
	preds = model("아마존 스테인레스 히터 200W 알에이디 주식회사")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 3 \| 9.088 \| 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 \|
	\| 9.0 \| 50 \|
	\| 10.0 \| 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 \| 0.381 \| - \|
	\| 0.6329 \| 50 \| 0.2955 \| - \|
	\| 1.2658 \| 100 \| 0.1053 \| - \|
	\| 1.8987 \| 150 \| 0.0527 \| - \|
	\| 2.5316 \| 200 \| 0.0275 \| - \|
	\| 3.1646 \| 250 \| 0.0285 \| - \|
	\| 3.7975 \| 300 \| 0.0132 \| - \|
	\| 4.4304 \| 350 \| 0.0203 \| - \|
	\| 5.0633 \| 400 \| 0.0133 \| - \|
	\| 5.6962 \| 450 \| 0.01 \| - \|
	\| 6.3291 \| 500 \| 0.0106 \| - \|
	\| 6.9620 \| 550 \| 0.0013 \| - \|
	\| 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 \| 0.0 \| - \|
	\| 12.6582 \| 1000 \| 0.0 \| - \|
	\| 13.2911 \| 1050 \| 0.0001 \| - \|
	\| 13.9241 \| 1100 \| 0.0 \| - \|
	\| 14.5570 \| 1150 \| 0.0001 \| - \|
	\| 15.1899 \| 1200 \| 0.0 \| - \|
	\| 15.8228 \| 1250 \| 0.0 \| - \|
	\| 16.4557 \| 1300 \| 0.0 \| - \|
	\| 17.0886 \| 1350 \| 0.0 \| - \|
	\| 17.7215 \| 1400 \| 0.0 \| - \|
	\| 18.3544 \| 1450 \| 0.0 \| - \|
	\| 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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