master_cate_ac7 / 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: 가스코 가죽전용염색약 소파 카시트 스니커즈 33색상 100ml 다크브라운 주식회사가스코
	- text: 레인슈즈 장화 방수 부츠 수중작업 신발보호 고무 미끄럼방지 여성용 H_M 34-36 지에스
	- text: 가스코 가죽전용염색약 도구 풀세트 가죽옷 100ml 브라운_무광 주식회사 가스코
	- text: 엑스솔 에어슬림 인솔 기능성 신발 깔창 245mm 주식회사 영창에코
	- text: 깁스 양말 싸개 발 보호 보온 방한 편한 이쁜 롱 부츠형 여성 방수커버 샤워 팔 남성용 플러시 슬리브/두꺼운 버전 높이 35_45
	핑크고릴라
	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: 0.9254610935283204
	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 \|
	\|:------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 6.0 \| <ul><li>'등산화끈 1+1 통끈_라인네이비 신세계몰'</li><li>'등산화끈 1+1 트위스트_브라운 신세계몰'</li><li>'몽벨 슈레이스 플랫 4MM YELLOW JBSXXUZZ105 신발끈 평끈 등산화끈 140 (주)코어밸류'</li></ul> \|
	\| 2.0 \| <ul><li>'고급 강남스타힐 구두굽/소음방지/충격완화/하이힐굽 블랙_DD-107 슈미즈'</li><li>'발 뒤꿈치 패드 쿠션 신발 구두 운동화 사이즈 클때 줄이기 패드 6-피넛_아이보리화이트_One Size(2P) 저스트에잇'</li><li>'고급 강남스타힐 구두굽/소음방지/충격완화/하이힐굽 블랙_DD-092 슈미즈'</li></ul> \|
	\| 5.0 \| <ul><li>'[웰럽] 시그니처 깔창 아치 운동화 등산화 군대 군인 군화 안전화 평발 기능성 키높이 [0008]그린 M(255 270) CJONSTYLE'</li><li>'[롯데백화점]에코(슈즈) 컴포트 에브리데이 인솔 멘즈 9059029-00101 블랙_EU 39 롯데백화점_'</li><li>'등산화 깔창 기능성 운동화 특수 스포츠 신발 키높이 골프화 XL275-295 마켓퀸즈'</li></ul> \|
	\| 0.0 \| <ul><li>'[현대백화점]금강제화 랜드로바 SHOSC0150SAM 휴대용 미니 구두헤라 [00001] 휴대용 구두칼 (주)현대홈쇼핑'</li><li>'에드가 체크 소가죽 휴대용 슈혼 navy 000 (주)트라이본즈'</li><li>'[금강제화](광주신세계) 콜렉션 휴대용 슈혼 스틸 미니 구두 헤라 N8MKA150/SHOSC0150SAM 10.5cm 신세계백화점'</li></ul> \|
	\| 4.0 \| <ul><li>'비오는날 남성 여성 1회용비닐덧신 S M L 비올때신발 여름필수품 신발우비 색상_레인신발커버 투명블루M 오픈리빙'</li><li>'비올때 이색적인 여성용 싱글 슈즈 가죽 신발 여성 패션 레인신발커버 멋스러운코디 13_39 스톰브랜상범'</li><li>'투명 슈즈 패션 워터 장마 여성장화 미끄럼방지 학생 XXL(43-45 적합)_블루-하이 [미끄럼방지창x2년 품질] 구룡글로벌'</li></ul> \|
	\| 1.0 \| <ul><li>'곰돌이 블랙 검정하트 화이트 남자 성인 커플 지비추 자비츠 심플 파츠 클로그 참 장식신발 A set (블랙-화이트) 뉴지(NYUZY)'</li><li>'슈팁 금속팁 메탈락팁 듀브레 메탈밴드 악어클립 메탈고정핀 플라스틱고정핀 골드슈팁 황동슈팁 실버슈팁 금속슈팁 굵은골드(4개) 슈레이스'</li><li>'(SAPHIR) 사피르 레노베이팅 컬러 재생크림 / 가죽 염색제 리노베이팅 미디엄브라운 제이엠컴퍼니'</li></ul> \|
	\| 3.0 \| <ul><li>'STRATTON 남성용 삼나무 슈트리- 미국산, m / 9 - 10 알쓰리컴퍼니'</li><li>'발볼 여자 신발 남자 제골기 발등 여성하이힐 발등 코코나라'</li><li>'슈샤이너 전기 금속제골기 경첩타입 전문가용 레지가다 업소용 여성용 js9997'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Metric \|
	\|:--------\|:-------\|
	\| all \| 0.9255 \|

	## 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_ac7")
	# Run inference
	preds = model("엑스솔 에어슬림 인솔 기능성 신발 깔창 245mm 주식회사 영창에코")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:--------\|:----\|
	\| Word count \| 3 \| 10.4257 \| 27 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0.0 \| 50 \|
	\| 1.0 \| 50 \|
	\| 2.0 \| 50 \|
	\| 3.0 \| 50 \|
	\| 4.0 \| 50 \|
	\| 5.0 \| 50 \|
	\| 6.0 \| 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 \| 0.3761 \| - \|
	\| 0.9091 \| 50 \| 0.2291 \| - \|
	\| 1.8182 \| 100 \| 0.033 \| - \|
	\| 2.7273 \| 150 \| 0.018 \| - \|
	\| 3.6364 \| 200 \| 0.0001 \| - \|
	\| 4.5455 \| 250 \| 0.0001 \| - \|
	\| 5.4545 \| 300 \| 0.0001 \| - \|
	\| 6.3636 \| 350 \| 0.0001 \| - \|
	\| 7.2727 \| 400 \| 0.0001 \| - \|
	\| 8.1818 \| 450 \| 0.0 \| - \|
	\| 9.0909 \| 500 \| 0.0 \| - \|
	\| 10.0 \| 550 \| 0.0 \| - \|
	\| 10.9091 \| 600 \| 0.0 \| - \|
	\| 11.8182 \| 650 \| 0.0 \| - \|
	\| 12.7273 \| 700 \| 0.0 \| - \|
	\| 13.6364 \| 750 \| 0.0 \| - \|
	\| 14.5455 \| 800 \| 0.0 \| - \|
	\| 15.4545 \| 850 \| 0.0 \| - \|
	\| 16.3636 \| 900 \| 0.0 \| - \|
	\| 17.2727 \| 950 \| 0.0001 \| - \|
	\| 18.1818 \| 1000 \| 0.0 \| - \|
	\| 19.0909 \| 1050 \| 0.0 \| - \|
	\| 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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