master_cate_el2 / README.md

Push model using huggingface_hub.

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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: IPTIME UC 305HDMI C타입 USB 멀티포트 노트북 확장 PD (주)스마트포유
	- text: 로지텍 파워플레이 Logitech Powerplay 시스템 충전패드 병행수입 Power Play 주식회사 데나
	- text: PBT키캡 푸딩 이중사출 영문 정각 108 풀배열 키보드 화이트 몬스타 주식회사
	- text: 펠로우즈 i-spire rocking 미니손목받침대 그레이 93933 그레이 아이룸코리아
	- text: AMH 클리어 투웨이 4포트 USB3.0 허브 민트 주식회사보성닷컴
	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: 0.9550144449030128
	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: 9 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 \|
	\|:------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 2 \| <ul><li>'몬스타기어 달토끼 PBT 체리 프로파일 키캡 주식회사 노벨뷰사이언스'</li><li>'[COX] 영문 키캡, CX158 158키 이색사출 PBT 키캡, OSA 프로파일 [오셀라리스] (주)컴퓨존'</li><li>'벤큐 조위 CAMADE2 e-Sports 게이밍 마우스 번지대/마우스번지/카마데2 하이스트네트웍스 주식회사'</li></ul> \|
	\| 5 \| <ul><li>'지클릭커 클라우드 코튼 팜레스트 키보드 쿠션 손목 받침대 눈설탕 눈설탕 (주)수빈인포텍'</li><li>'ABKO ARC1 TKL 아크릴 팜레스트 키보드 손목 받침대 텐키리스용 아이스 아크릴 조은 정보'</li><li>'펠로우즈 크리스탈젤 미니손목받침대 CRC91477 / 보라 에이티쓰리'</li></ul> \|
	\| 8 \| <ul><li>'로지텍 K380 키스킨 주식회사 제이앤디코퍼레이션'</li><li>'로지텍 K260 K270 K275 K295 MK275 MK295 키스킨 키보드커버 덮개 로지텍 K295 키스킨 현민트레이딩 주식회사'</li><li>'로지텍 K270 MK270R MK260R 키보드보호 키스킨 유비스마트'</li></ul> \|
	\| 4 \| <ul><li>'지클릭커 모니터 필름 PET 부착식 정보 보안 노트북 화면 보호기 블루라이트 차단 12.5인치 현시스템'</li><li>'앱코 블루라이트 차단 양면 부착형 모니터 정보보안필름 와이드(16:9) IP-24W 주식회사 케이에스샵'</li><li>'펠로우즈 프라이버시 정보보안 필터 14.1인치 와이드 16:10 정보보호 필름 48006 와이티코리아 주식회사'</li></ul> \|
	\| 3 \| <ul><li>'앱코 Pastel Desk Long Pad 마우스패드 파스텔 베이지 주식회사 승호'</li><li>'스틸시리즈 Qck Edge XL 게이밍 마우스패드 주식회사 엠앤웍스'</li><li>'파스텔 방수 가죽 마우스 장패드 네이비 본조르노온라인 주식회사'</li></ul> \|
	\| 7 \| <ul><li>'동성 만능크리너 60매 본품 (주)바오밥컴퍼니'</li><li>'동성크리너 동성 만능크리너 150매 (원통형) 주식회사 해인디지탈'</li><li>'일신 ECC-90 전기접점부활제 250g 리모콘 플스 닌텐도 스위치 조이콘 조이스틱 쏠림 접점세척제 벡스 BW-100 전기접점부활제 225g 모멘트리 (MOMENTREE)'</li></ul> \|
	\| 6 \| <ul><li>'전오 케이블타이 450mm 대용량 흰색 J-450 100개 국산 손소프트'</li><li>'베이스어스 마그네틱 케이블클립,선정리,케이블홀더 블랙(ACWDJ-01) 주식회사엠피맨코리아'</li><li>'전오 케이블타이 140MM 국산제품 전선정리 포장끈 작업현장 건설 농장 전자 공장 백색(1000개) 보람 LED'</li></ul> \|
	\| 1 \| <ul><li>'ipTIME UH505 (기본구성) USB3.0 5포트 USB허브 5V3A 어댑터 (주)즐찾'</li><li>'EFM네트웍스 아이피타임 UH505 다사다 유한책임회사'</li><li>'벨킨 11in1 USB C타입 멀티 허브 독 100W 충전 HDMI VGA 이더넷 노트북 거치대형 INC004bt 아이폰15 갤럭시 S24 그램 맥북 노트북 호환 실버그레이(INC004btSGY) (주) 디지월드'</li></ul> \|
	\| 0 \| <ul><li>'Coms DJ729 데스크탑 PC 이동형 스탠드 컴퓨터 본체 거치대 바퀴 이동식 블랙 루미너스'</li><li>'컴퓨터 본체 받침대 DJ729 주식회사보성닷컴'</li><li>'데스크탑 PC 본체 이동형 스탠드 DJ729 주식회사 지디스엠알오'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Metric \|
	\|:--------\|:-------\|
	\| all \| 0.9550 \|

	## 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_el2")
	# Run inference
	preds = model("AMH 클리어 투웨이 4포트 USB3.0 허브 민트 주식회사보성닷컴")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:--------\|:----\|
	\| Word count \| 4 \| 10.1397 \| 25 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0 \| 8 \|
	\| 1 \| 50 \|
	\| 2 \| 50 \|
	\| 3 \| 50 \|
	\| 4 \| 50 \|
	\| 5 \| 50 \|
	\| 6 \| 50 \|
	\| 7 \| 50 \|
	\| 8 \| 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0156 \| 1 \| 0.4963 \| - \|
	\| 0.7812 \| 50 \| 0.1854 \| - \|
	\| 1.5625 \| 100 \| 0.046 \| - \|
	\| 2.3438 \| 150 \| 0.0048 \| - \|
	\| 3.125 \| 200 \| 0.0168 \| - \|
	\| 3.9062 \| 250 \| 0.0002 \| - \|
	\| 4.6875 \| 300 \| 0.0001 \| - \|
	\| 5.4688 \| 350 \| 0.0001 \| - \|
	\| 6.25 \| 400 \| 0.0001 \| - \|
	\| 7.0312 \| 450 \| 0.0001 \| - \|
	\| 7.8125 \| 500 \| 0.0001 \| - \|
	\| 8.5938 \| 550 \| 0.0001 \| - \|
	\| 9.375 \| 600 \| 0.0001 \| - \|
	\| 10.1562 \| 650 \| 0.0001 \| - \|
	\| 10.9375 \| 700 \| 0.0 \| - \|
	\| 11.7188 \| 750 \| 0.0001 \| - \|
	\| 12.5 \| 800 \| 0.0 \| - \|
	\| 13.2812 \| 850 \| 0.0 \| - \|
	\| 14.0625 \| 900 \| 0.0 \| - \|
	\| 14.8438 \| 950 \| 0.0 \| - \|
	\| 15.625 \| 1000 \| 0.0 \| - \|
	\| 16.4062 \| 1050 \| 0.0001 \| - \|
	\| 17.1875 \| 1100 \| 0.0 \| - \|
	\| 17.9688 \| 1150 \| 0.0 \| - \|
	\| 18.75 \| 1200 \| 0.0 \| - \|
	\| 19.5312 \| 1250 \| 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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