master_cate_lh1 / 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: 아이워너 스마트체지방체중계KS-BF4000/홈트용품/헬스용품 더베스트샾
	- text: 트랜스텍 팔뚝형 가정용 자동 혈압계 혈압측정기 TMB-1597 상승가압방식 바이메드
	- text: 휴비딕 초음파 무선 신장계 HUK-2 아기 키측정기 키재기 자동 거리 G 핑크 골든 플레이스
	- text: 앳플리 T9 정확한 몸무게 저울 더블스마트인 체중계 가정용 전자 기계 화이트 (주)픽스몰
	- text: 어린이 신장 측정기 높이 벽 스티커 3D 키재기 신장계 B_큰 핑팝
	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: 0.971224790949336
	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: 7 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 \|
	\|:------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 1.0 \| <ul><li>'신장 측정 기계 표준 기계식 측정기 학교 병원 약국 건강 검진 신장계 키재기 70-190cm 블랙_기계적 핑팝'</li><li>'공룡 자석 귀여운 키재기 기린 눈금 벽 측정도구 소프트웨어 보내기[프로 버전-양고] 고소몽 새벽잡화점'</li><li>'키 몸무게 측정기 검사 헬스장 학교 검진 보건실 신체 160kg 제품 (블랙) 노마둔'</li></ul> \|
	\| 5.0 \| <ul><li>'휴비딕 신생아 유아 아기 고양이 강아지 반려동물 체중계 HUS-316B (주)휴비딕'</li><li>'[애구애구] 강아지 고양이 체중계 건전지 포함, 애견 원터치 무선 체중계, 반려견 몸무계 측정기 애드마스터'</li><li>'상업용 전자 정밀 소형 저울T사우나 헬스장 체중계 전자저울 100KG 150KG 이로운발견'</li></ul> \|
	\| 0.0 \| <ul><li>'스마트 만보기 시계 만보팔찌 손목만보계 칼로리시계 스마트 만보 시계 팔찌 손목 형 실리콘 디지털 계 만보기시계-민트 제이한 주식회사'</li><li>'오리온 계수기 FH102 주식회사 다원피앤피'</li><li>'미니 디지털카운터기 0~99999까지 / 반지계수기 카운터기-파랑 대박나라'</li></ul> \|
	\| 4.0 \| <ul><li>'브라운 써모스캔 귀 체온계 IRT6030 롯데백화점1관'</li><li>'브라운체온계 IRT-6030 적외선 귀체온계 가정용 신생아 체온계 필터21개+건전지 포함 브라운체온계 IRT-6030 주식회사 온라이브플러스'</li><li>'브라운 귀체온계 IRT-6030 + 필터21p포함/1년무상AS baby 신세계몰'</li></ul> \|
	\| 6.0 \| <ul><li>'오므론 손목형 자동전자 혈압계 HEM-6161 가정용혈압계_MC 멸치쇼핑'</li><li>'인바디 BPBIO320N 자동 혈압계 BPBIO320N_그레이(테이블+의자 포함) 바디메디칼'</li><li>'휴비딕 비피첵 손목 자동 전자 혈압계 HBP-600 혈압측정기 판테온'</li></ul> \|
	\| 3.0 \| <ul><li>'독일 LED 검이경(성인/아동 겸용)-건전지식 풍솔글로벌'</li><li>'간호사용 병원 진찰용품 의사 청진기 측정기 소아과 심박 내과 cosse2'</li><li>'SPIRIT 검이경 CK-939A /오토스코프/직접조사방식/알루미늄재질/좌우스위치방식적용 풍솔글로벌'</li></ul> \|
	\| 2.0 \| <ul><li>'Wahoo Fitness 티커 심박수측정기(HRM) 스텔스 그레이 White 픽더마인드'</li><li>'POLAR Equine H10 라이딩 심박수 센서 라이브러리2'</li><li>'Polar H10 심박수 모니터, 블루투스 HRM 가슴 스트랩 - 아이폰 및 안드로이드 호환, 블랙 식스퀄리티'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Metric \|
	\|:--------\|:-------\|
	\| all \| 0.9712 \|

	## 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_lh1")
	# Run inference
	preds = model("어린이 신장 측정기 높이 벽 스티커 3D 키재기 신장계 B_큰 핑팝")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 3 \| 9.9771 \| 18 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0.0 \| 50 \|
	\| 1.0 \| 50 \|
	\| 2.0 \| 6 \|
	\| 3.0 \| 50 \|
	\| 4.0 \| 50 \|
	\| 5.0 \| 50 \|
	\| 6.0 \| 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0208 \| 1 \| 0.4314 \| - \|
	\| 1.0417 \| 50 \| 0.1947 \| - \|
	\| 2.0833 \| 100 \| 0.0912 \| - \|
	\| 3.125 \| 150 \| 0.0968 \| - \|
	\| 4.1667 \| 200 \| 0.0231 \| - \|
	\| 5.2083 \| 250 \| 0.0004 \| - \|
	\| 6.25 \| 300 \| 0.0001 \| - \|
	\| 7.2917 \| 350 \| 0.0001 \| - \|
	\| 8.3333 \| 400 \| 0.0 \| - \|
	\| 9.375 \| 450 \| 0.0001 \| - \|
	\| 10.4167 \| 500 \| 0.0 \| - \|
	\| 11.4583 \| 550 \| 0.0 \| - \|
	\| 12.5 \| 600 \| 0.0 \| - \|
	\| 13.5417 \| 650 \| 0.0 \| - \|
	\| 14.5833 \| 700 \| 0.0 \| - \|
	\| 15.625 \| 750 \| 0.0 \| - \|
	\| 16.6667 \| 800 \| 0.0 \| - \|
	\| 17.7083 \| 850 \| 0.0 \| - \|
	\| 18.75 \| 900 \| 0.0 \| - \|
	\| 19.7917 \| 950 \| 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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