master_cate_el12 / 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: 한글과컴퓨터 한컴오피스 2024 한글 Open 라이선스 [기업용/영구/2User이상] 한컴오피스 2024 (한글/한셀/한쇼) (주)유비소프트웨어
	- text: 한글과컴퓨터 한글 2022 (기업용/패키지/USB방식) 아이코다(주)
	- text: 한글과컴퓨터 한컴독스 기업용 ESD 1년 사용 (주)대성클라우드
	- text: '[한글과컴퓨터] 한컴오피스 2022 [기업용/패키지/1년사용/제품키배송형] (주)컴퓨존'
	- text: '[마이크로소프트코리아] MS Windows 7 Professional DSP 한글 64bit/정품라벨 (주)소프트존'
	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: 1.0
	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: 6 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 \|
	\|:------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 4 \| <ul><li>'정품 스토어 MS Windows 11 Home 한글 FPP 윈도우11 홈 설치USB 패키지 인증키 (주)에스비코어'</li><li>'윈도우11 프로 FPP(USB) 노트북 업그레이드 전용상품 주식회사 이좋은세상'</li><li>'[MS코리아정품] Windows 11 Pro FPP 한글 처음사용자용 영구 제품키 주식회사 레오솔루션'</li></ul> \|
	\| 1 \| <ul><li>'[Adobe] Photoshop for teams [기업용/라이선스/1년사용] [1개~9개 구매시(1개당 가격)] [발송 3~7일 소요] 갱신 (주)컴퓨존'</li><li>'Movavi Video Editor 2024 기업용 라이선스 / 모바비 주식회사 글래드소프트'</li><li>'Movavi Video Suite 2024 공공기관용 라이선스 / 모바비2024 메모리콕'</li></ul> \|
	\| 2 \| <ul><li>'안랩 V3 Net for Windows Server 9.0 DSP (1년) (주)위프로소프트'</li><li>'안랩 V3 Net for Windows Server 9.0 (기업용/DSP/1년) 아이코다(주)'</li><li>'안랩 V3 Net for Unix Server (기업용 1년사용) 아이코다(주)'</li></ul> \|
	\| 3 \| <ul><li>'[문자발송]한컴독스 개인용 1년(구독형 한컴오피스) / 윈도우 맥용 설치 파일 지원 주식회사 지엘스토어'</li><li>'한컴독스 개인용 1년 제품키배송형(구독형 한컴오피스) / 윈도우 맥용 설치 파일 지원 확인 주식회사 라이프큐브'</li><li>'[마이크로소프트] Office 2019 Home & Student PKC [가정용/패키지/한글] 택배 발송 오시리스랩 주식회사'</li></ul> \|
	\| 5 \| <ul><li>'[1분발송]리훈 오늘기억 일기장 다이어리 굿노트 아이패드 PDF 속지 3년 감사 1.오른손잡이용_1.3년다이어리 주식회사 리훈 (RIHOON CO., LTD.)'</li><li>'[스티커2종] 24년 오리지날 굿노트 디지털 속지 - 데일리 가로형(1D2P 형식) (아이패드 갤럭시탭 하이퍼링크 PDF 속지) (주)프랭클린 플래너 코리아'</li><li>'[1분발송]리훈 하고싶은말 일기장 다이어리 굿노트 아이패드 PDF 속지 날짜형（23년10월-24년12월）_오른손잡이용 주식회사 리훈 (RIHOON CO., LTD.)'</li></ul> \|
	\| 0 \| <ul><li>'Radmin 3 Standard license 기업용/ 영구(ESD) (주)삼경엠'</li><li>'Radmin 3 - 50 Licenses Pack 기업용 라이선스 /알어드민 / 원격지원 / 50대설치 메모리콕'</li><li>'Radmin 3 Standard 기업용 라이선스 /알어드민 / 원격지원 메모리콕'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Metric \|
	\|:--------\|:-------\|
	\| all \| 1.0 \|

	## 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_el12")
	# Run inference
	preds = model("한글과컴퓨터 한컴독스 기업용 ESD 1년 사용 (주)대성클라우드")
	```

	<!--
	### Downstream Use

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:--------\|:----\|
	\| Word count \| 6 \| 11.8852 \| 21 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0 \| 3 \|
	\| 1 \| 34 \|
	\| 2 \| 33 \|
	\| 3 \| 50 \|
	\| 4 \| 50 \|
	\| 5 \| 13 \|

	### 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0345 \| 1 \| 0.496 \| - \|
	\| 1.7241 \| 50 \| 0.0031 \| - \|
	\| 3.4483 \| 100 \| 0.0001 \| - \|
	\| 5.1724 \| 150 \| 0.0 \| - \|
	\| 6.8966 \| 200 \| 0.0 \| - \|
	\| 8.6207 \| 250 \| 0.0 \| - \|
	\| 10.3448 \| 300 \| 0.0 \| - \|
	\| 12.0690 \| 350 \| 0.0 \| - \|
	\| 13.7931 \| 400 \| 0.0 \| - \|
	\| 15.5172 \| 450 \| 0.0 \| - \|
	\| 17.2414 \| 500 \| 0.0 \| - \|
	\| 18.9655 \| 550 \| 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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