master_cate_el13 / 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: LG전자 올레드 TV OLED55C2FNA 스탠드 윤성 운송료상이 윤성종합가전
	- text: '[엡손] EH-LS500W / 4K UHD 4000안시 2,500,000:1 EPSON 빔 프로젝터 초단초점 (주)메리트정보'
	- text: 루컴즈 2024년형 50인치 스마트 UHD 구글 TV 4K 에너지효율 1등급 T5003KUG 스탠드 빌리어네어디
	- text: 이노스 S8601KU LG 패널 스마트 TV 구글티비 벽걸이 기사방문설치(브라켓별도)_수도권(서울경기인천)_86인치 QLED 구글TV
	(주)티지디지털
	- text: 삼성 WMN4070SG 벽결이브라켓 삼성고정브라켓 두루엠에스
	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: 0.763001415762152
	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 \|
	\|:------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 6 \| <ul><li>'윤씨네 J-SV / 족자스크린 4:3비율 100인치 에스앤피'</li><li>'[ FLAT FLOW ] 플랏플로우 100인치 와이드 분리형 족자스크린 F-HJ100W F-HJ100W (100인치 와이드 족자형) 아이티원'</li><li>'윤씨네 J-SH40 / 와이드 족자스크린 16:9 40인치 에스앤피'</li></ul> \|
	\| 2 \| <ul><li>'75인치 189cm 4K UHD 비즈니스TV LH75BECH 스탠드 에너지효율등급 1등급 우수한 내구성 주식회사 쇼핑하는니체'</li><li>'[LG] 55인치 UHD 단독형 사이니지 3시리즈 (55UL3J) 고정형 벽걸이 설치 주식회사 케이엠시스템'</li><li>'[LG] 55인치 비디오월 슬림 베젤 1.74 mm, 500nit (55VM5J) 벽걸이 설치 (별도문의) 주식회사 케이엠시스템'</li></ul> \|
	\| 5 \| <ul><li>'벤큐 GS50 풀HD 캠핑용 빔프로젝터 안드로이드 아이폰 무선미러링 배터리내장 블루투스 (주)아솔컴퍼니'</li><li>'에이서 DX227 🧡정품 신형🧡 5200안시 XGA 20000:1 DLP 회의용 교육용 강당용 멀티용 도움에이브이'</li><li>'[피제이시스] 엡손 EB-L1070U 레이저프로젝터 ❤️정품새상품 ❤️ 주식회사 피제이시스(PJSYS.co.Ltd.)'</li></ul> \|
	\| 0 \| <ul><li>'이노스 S2401KU 어반스톡'</li><li>'[무결점] 프리즘 바이런 75인치 1등급 4K HDR 베젤리스TV 패널 2년 무상보증 / BR750UD_기사설치포함 (주)프리즘코리아'</li><li>'[무결점] 프리즘 바이런 55인치 1등급 4K HDR 베젤리스TV 패널 2년 무상보증 / BR550UHD (주)프리즘코리아'</li></ul> \|
	\| 4 \| <ul><li>'[PICO 국내 공식판매처] PICO NEO3 Enterprise VR (256GB) / 공공기관 및 공공교육기관 전용 주식회사 메타에듀시스'</li><li>'에듀플레이어 EA400 DVD플레이어 CD/DVD리핑 투웨이 블루투스 EA400 (ED404) 주식회사 에듀플레이어'</li><li>'오큘러스 퀘스트2 Oculus Quest2 올인원 VR게임헤드셋 퀘스트2 128GB (관세 대납) 팽마켓'</li></ul> \|
	\| 1 \| <ul><li>'카멜 디지털액자화이트(블랙) / PF1040IPS /10인치 디지털액자(동영상,슬리이드쇼,앨범) 선물용디지털액자PF-1040IPS / 디지털사진액자/ 16:9화면(화이트or 블랙) 블랙 에스라B2B'</li><li>'컴스마트 BM170 15.4형 스마트 디지털 액자 동영상 시계 달력 HDMI 서브 모니터 블루시스템쇼핑몰 주식회사'</li><li>'카멜 디지털액자 10인치 PF-1040IPS 미니모니터 사진 동영상 음악 에스제이인터내셔널'</li></ul> \|
	\| 3 \| <ul><li>'COMBO-2000A (금영 (KY)/ 내셔널 (NATIONAL) / 넥스디지탈 (NEX) /넥슨 (NEXN) /뉴썬인더스트리 엔플러스(NPLUS)/ 다비디스플레이 (DAVI) COMBO-2000A 메카트로주식회사'</li><li>'COMBO-119 /APH13000/AP-H3020/AP-H4000/APH-H2300/AP-HH232N/IAS-T1010/IAS-T810/IAS-T82CA 지에이치스토어'</li><li>'COMBO-2201 (AKB75455603 / AKB75635301 / AKB75635305 / AKB75675304 / akb75675306 / AKB75755301) 메카트로'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Metric \|
	\|:--------\|:-------\|
	\| all \| 0.7630 \|

	## 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_el13")
	# Run inference
	preds = model("삼성 WMN4070SG 벽결이브라켓 삼성고정브라켓 두루엠에스")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:--------\|:----\|
	\| Word count \| 3 \| 10.4229 \| 25 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0 \| 50 \|
	\| 1 \| 50 \|
	\| 2 \| 50 \|
	\| 3 \| 50 \|
	\| 4 \| 50 \|
	\| 5 \| 50 \|
	\| 6 \| 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0182 \| 1 \| 0.4965 \| - \|
	\| 0.9091 \| 50 \| 0.118 \| - \|
	\| 1.8182 \| 100 \| 0.0382 \| - \|
	\| 2.7273 \| 150 \| 0.0008 \| - \|
	\| 3.6364 \| 200 \| 0.0003 \| - \|
	\| 4.5455 \| 250 \| 0.0002 \| - \|
	\| 5.4545 \| 300 \| 0.0002 \| - \|
	\| 6.3636 \| 350 \| 0.0002 \| - \|
	\| 7.2727 \| 400 \| 0.0001 \| - \|
	\| 8.1818 \| 450 \| 0.0001 \| - \|
	\| 9.0909 \| 500 \| 0.0001 \| - \|
	\| 10.0 \| 550 \| 0.0001 \| - \|
	\| 10.9091 \| 600 \| 0.0001 \| - \|
	\| 11.8182 \| 650 \| 0.0001 \| - \|
	\| 12.7273 \| 700 \| 0.0001 \| - \|
	\| 13.6364 \| 750 \| 0.0001 \| - \|
	\| 14.5455 \| 800 \| 0.0001 \| - \|
	\| 15.4545 \| 850 \| 0.0001 \| - \|
	\| 16.3636 \| 900 \| 0.0001 \| - \|
	\| 17.2727 \| 950 \| 0.0001 \| - \|
	\| 18.1818 \| 1000 \| 0.0001 \| - \|
	\| 19.0909 \| 1050 \| 0.0001 \| - \|
	\| 20.0 \| 1100 \| 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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