master_cate_el7 / 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: 삼성 노트북 NT450R5E K81S K82P K82W K83S K85S 정품 어댑터 아답터 아답타 충전기 AD-6019R 19V 3.16A 뉴
	스마트 전자
	- text: 인트존 205X 노트북 파우치 13인치 15인치 핸디 가방 13인치_스모키블랙 크로니시스템
	- text: 엑토(ACTTO) NBL-04 노트북 도난방지 케이블/(화이트) 국진컴퓨터
	- text: 삼성 정품어댑터AD-4019A/19V2.1A/NT930X5J-K82S/4019P 엔티와이
	- text: LG 그램 17Z90SP & 17ZD90SP 17인치 퓨어 저반사 지문방지 액정보호필름 제트비컴퍼니
	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: 0.9272844272844273
	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 \|
	\|:------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 8 \| <ul><li>'MSI 프레스티지 16 AI Evo B1MG 노트북 키스킨 커버 무소음 키보드 올유어리브'</li><li>'맥북 에어 15인치 키스킨 M2 실리콘 키보드덮개 (주)스코코'</li><li>'삼성갤럭시북3 Go 키스킨 NT345XPA-KC04S 키스킨 키커버 14인치 실리스킨 문자인쇄 키스킨(블랙) 에이플'</li></ul> \|
	\| 0 \| <ul><li>'칼디짓 엘레멘트독 CalDigit Element Dock 썬더볼트4 독 멀티허브 맥북프로 Element Dock (주)디엔에이치'</li><li>'마하링크 2.5인치 SATA 멀티부스트 ML-MBS127 디메이드 (DMADE)'</li><li>'AA-AE2N12B usb 젠더 컴퓨터 인터넷 설치 랜 포트 에스아이'</li></ul> \|
	\| 3 \| <ul><li>'잘만 ZM-NS1000 정품/노트북 받침대/쿨링패드 주식회사보성닷컴'</li><li>'-잘만 ZM-NS1 (블랙)- 주식회사 케이에이치몰'</li><li>'잘만 노트북 쿨링 받침대 ZM-NS2000 (주)아싸컴'</li></ul> \|
	\| 5 \| <ul><li>'W01 HP Omen 17-ANxxxTX 시리즈용 Crystal액정보호필름 더블유공일'</li><li>'맥북 에어 15인치 필름 M2 무광 하판 외부 1매 무광 상판 1매 (주)스코코'</li><li>'맥북에어 M3 2024 15인치 외부보호필름 3종세트 에이엠스토어'</li></ul> \|
	\| 1 \| <ul><li>'이지엘 국산 가벼운 손잡이 노트북 파우치 케이스 13.3인치 For 13.3인치_스모키블랙 이지엘'</li><li>'[에버키] Titan 타이탄 EKP120 18.4인치 비투비마스터'</li><li>'LG 그램 14인치 전용 가죽 파우치 (주) 티앤티정보 용산전자랜드지점'</li></ul> \|
	\| 6 \| <ul><li>'[프라임디렉트] 아답터, 220V / 19V 3.42A [내경2.1~2.5mm/외경5.5mm] 전원 케이블 미포함 [비닐포장] (주)컴퓨존'</li><li>'삼성 정품 노트북 NT-RV720 / 19V 3.16A AD-6019S AD-6019R 정품 전원 어댑터 고다'</li><li>'EFM ipTIME 어댑터 48V-0.5A (ipTIME 제품군 호환용) [ 아이피타임 ] (주)클럽라이더'</li></ul> \|
	\| 7 \| <ul><li>'HP 노트북배터리 14 15 TPN-Q207 Q208 HT03XL 호환용배터리 라온하람몰'</li><li>'(AA-PB9NC6B)삼성 정품 노트북 배터리/NT-RF410 RF411 RF510 RF511 RF710 RF711 전용 엔티와이'</li><li>'삼성 정품 배터리 AA-PB9NC6B/NT-R530 R540 전용 노트북 배터리/ NTY 엔티와이'</li></ul> \|
	\| 2 \| <ul><li>'강원전자 넷메이트 노트북 도난방지 USB포트 와이어 잠금장치 키 타입 NM-SLL05M 보다넷'</li><li>'노트북 도난방지 와이어 잠금장치 NM-SLL03 주식회사 루피하루'</li><li>'엑토(ACTTO) NBL-01 노트북 도난방지 케이블/잠금장치 국진컴퓨터'</li></ul> \|
	\| 4 \| <ul><li>'ASUS 비보북 15 X1504ZA 노트북보안필름 프라이버시 사생활보호 거치형 거치형보안필름_1장 한성'</li><li>'[1300K] HP 빅터스 16-SxxxxAN 거치식 양면 사생활보호필터F 엔에이치엔위투 주식회사'</li><li>'삼성전자 갤럭시북4 NT750XGL-XC51S 노트북보안필름 프라이버시 사생활보호 부착형 부착형보안필름_1장 원일'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Metric \|
	\|:--------\|:-------\|
	\| all \| 0.9273 \|

	## 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_el7")
	# Run inference
	preds = model("엑토(ACTTO) NBL-04 노트북 도난방지 케이블/(화이트) 국진컴퓨터")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:--------\|:----\|
	\| Word count \| 4 \| 10.3626 \| 23 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0 \| 50 \|
	\| 1 \| 50 \|
	\| 2 \| 50 \|
	\| 3 \| 50 \|
	\| 4 \| 22 \|
	\| 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0152 \| 1 \| 0.4966 \| - \|
	\| 0.7576 \| 50 \| 0.184 \| - \|
	\| 1.5152 \| 100 \| 0.037 \| - \|
	\| 2.2727 \| 150 \| 0.0256 \| - \|
	\| 3.0303 \| 200 \| 0.0014 \| - \|
	\| 3.7879 \| 250 \| 0.0002 \| - \|
	\| 4.5455 \| 300 \| 0.0006 \| - \|
	\| 5.3030 \| 350 \| 0.0001 \| - \|
	\| 6.0606 \| 400 \| 0.0001 \| - \|
	\| 6.8182 \| 450 \| 0.0001 \| - \|
	\| 7.5758 \| 500 \| 0.0001 \| - \|
	\| 8.3333 \| 550 \| 0.0001 \| - \|
	\| 9.0909 \| 600 \| 0.0001 \| - \|
	\| 9.8485 \| 650 \| 0.0001 \| - \|
	\| 10.6061 \| 700 \| 0.0001 \| - \|
	\| 11.3636 \| 750 \| 0.0001 \| - \|
	\| 12.1212 \| 800 \| 0.0001 \| - \|
	\| 12.8788 \| 850 \| 0.0001 \| - \|
	\| 13.6364 \| 900 \| 0.0001 \| - \|
	\| 14.3939 \| 950 \| 0.0001 \| - \|
	\| 15.1515 \| 1000 \| 0.0001 \| - \|
	\| 15.9091 \| 1050 \| 0.0001 \| - \|
	\| 16.6667 \| 1100 \| 0.0001 \| - \|
	\| 17.4242 \| 1150 \| 0.0 \| - \|
	\| 18.1818 \| 1200 \| 0.0 \| - \|
	\| 18.9394 \| 1250 \| 0.0 \| - \|
	\| 19.6970 \| 1300 \| 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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