master_cate_el18 / 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: Pulsar X2V2 미니 무선 게이밍 마우스 (블랙) 와이에스비투비
	- text: TOSHIBA B-EX4T2 바코드프린터 산업용프린터 라벨프린터 203DPI_USB ㈜비티에스홀딩스
	- text: '[당일출고]삼성전자 SL-J1680 컬러잉크젯 복합기 인쇄+복사+스캔 [정품잉크포함] 제일프린텍'
	- text: 지클릭커 슈퍼히어로 SPK100 저소음 유선 무선 블루투스 레인보우 백라이트 기계식 게임용 키보드 (레트로 레드) (주)피씨베이스
	- text: NIIMBOT 님봇 D110 라벨기 휴대용 라벨프린터 라벨1롤포함 빅마운트앤컴퍼니
	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: 0.8548111301103685
	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 \|
	\|:------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 7 \| <ul><li>'와콤 CTL-472 웹툰 입문용 타블렛 펜 온라인강의 주식회사 지디스엠알오'</li><li>'와콤 타블렛 CTL-4100 와콤인튜어스 웹툰 (주)코티니'</li><li>'와콤 신티크16 DTK-1660 케이에이씨앤씨'</li></ul> \|
	\| 1 \| <ul><li>'브라더공식판매대리점 DCP-T426W 무한잉크복합기 인쇄 복사 스캔 무선 AS연장 (주)대명아이티'</li><li>'교세라 ECOSYS M5521cdn 컬러레이저복합기 정품토너포함 한라테크'</li><li>'DCP-T720DW 브라더정품 무한잉크복합기 인쇄 복사 스캔 자동양면인쇄 (주)진전산시스템'</li></ul> \|
	\| 4 \| <ul><li>'로지텍 코리아 미니멀 무선 일루미네이티드 키보드 MX KEYS MINI 블랙(그라파이트) 주식회사 자강정보통신'</li><li>'앱코 K660 축교환 완전방수 게이밍 카일광축 레인보우LED 블랙,리니어 에스티에스컴퍼니'</li><li>'ABKO HACKER K523 기계식 축교환 LED 키패드 주식회사 브라보세컨즈'</li></ul> \|
	\| 2 \| <ul><li>'브라더 TN-2380 정품토너 2.6K HL L2365DW HL L2360dn MFC L2700D MFC L2700DW 주식회사 휴먼아이티'</li><li>'삼성전자정품 폐토너통 CLT-W406/ C510W/ C513W/ C563W/ C563FW 엘케이솔루션'</li><li>'(HP) No.680 정품 F6V27AA 검정 정품잉크 검정 총1개만구매(2개이상주문시발송안됨) 밀알시스템'</li></ul> \|
	\| 6 \| <ul><li>'와콤원 펜 CP91300B2Z 삼성갤럭시탭,갤럭시노트,오닉스 호환 펜 '</li><li>'드로잉장갑 와콤 신티크 XP-PEN 휴이온 액정타블렛 아이패드 태블릿 터치오류방지 '</li><li>'드로잉장갑 와콤 신티크 XP-PEN 휴이온 액정타블렛 아이패드 태블릿 터치오류방지 '</li></ul> \|
	\| 8 \| <ul><li>'◆◆ 정품 샘플테이프 + ◆◆ 브라더 正品 이름 라벨스티커기계 PT-P900W QR코드 wifi ◀正品▶ PT-P900W 탑정보기술'</li><li>'가제트 3D펜 GP3000+5M PLA 필라멘트 세트(24색) (주)위드피플즈'</li><li>'인스탁스 와이드 링크 포토프린터 모카 그레이(+아크릴액자) 한국후지필름 (주)'</li></ul> \|
	\| 3 \| <ul><li>'엡손 DS-30000, 양면 스캐너 A3 주식회사 케이에스샵'</li><li>'엡손 WorkForce DS-50000 (주)테드이십일'</li><li>'엡손스캐너 ES-580WMLP 미니멀 라이프 패키지(ES-580W+재단기+롤러)북스캐너 (주)에이엔에이코리아'</li></ul> \|
	\| 5 \| <ul><li>'로지텍 MK295 SILENT WIRELESS COMBO (화이트) (주)아토닉스'</li><li>'로지텍 MK275 영문자판 병행수입 제이제이 인터내셔널'</li><li>'로지텍코리아 시그니처 MK650 무선 합본 (그래파이트) 주식회사 지엠샤이'</li></ul> \|
	\| 0 \| <ul><li>'ROCCAT KONE PRO AIR (블랙) (주)디아씨앤씨'</li><li>'[Logitech]로지텍 Trackman Marble USB 마우스 트랙맨 트랙볼 마블 마우스 벌크 /택배/병행/ 당일출고 Trackman Marble USB 허브포스트'</li><li>'로지텍 G402 Hyperion Fury (주)케이엘시스템'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Metric \|
	\|:--------\|:-------\|
	\| all \| 0.8548 \|

	## 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_el18")
	# Run inference
	preds = model("Pulsar X2V2 미니 무선 게이밍 마우스 (블랙) 와이에스비투비")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:--------\|:----\|
	\| Word count \| 4 \| 10.5569 \| 27 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0 \| 50 \|
	\| 1 \| 50 \|
	\| 2 \| 50 \|
	\| 3 \| 50 \|
	\| 4 \| 50 \|
	\| 5 \| 50 \|
	\| 6 \| 13 \|
	\| 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0154 \| 1 \| 0.4961 \| - \|
	\| 0.7692 \| 50 \| 0.1923 \| - \|
	\| 1.5385 \| 100 \| 0.0615 \| - \|
	\| 2.3077 \| 150 \| 0.0532 \| - \|
	\| 3.0769 \| 200 \| 0.0513 \| - \|
	\| 3.8462 \| 250 \| 0.0283 \| - \|
	\| 4.6154 \| 300 \| 0.0313 \| - \|
	\| 5.3846 \| 350 \| 0.0258 \| - \|
	\| 6.1538 \| 400 \| 0.0174 \| - \|
	\| 6.9231 \| 450 \| 0.0053 \| - \|
	\| 7.6923 \| 500 \| 0.0021 \| - \|
	\| 8.4615 \| 550 \| 0.0039 \| - \|
	\| 9.2308 \| 600 \| 0.0059 \| - \|
	\| 10.0 \| 650 \| 0.0001 \| - \|
	\| 10.7692 \| 700 \| 0.0001 \| - \|
	\| 11.5385 \| 750 \| 0.0001 \| - \|
	\| 12.3077 \| 800 \| 0.0001 \| - \|
	\| 13.0769 \| 850 \| 0.0001 \| - \|
	\| 13.8462 \| 900 \| 0.0 \| - \|
	\| 14.6154 \| 950 \| 0.0001 \| - \|
	\| 15.3846 \| 1000 \| 0.0 \| - \|
	\| 16.1538 \| 1050 \| 0.0 \| - \|
	\| 16.9231 \| 1100 \| 0.0 \| - \|
	\| 17.6923 \| 1150 \| 0.0 \| - \|
	\| 18.4615 \| 1200 \| 0.0 \| - \|
	\| 19.2308 \| 1250 \| 0.0 \| - \|
	\| 20.0 \| 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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