master_cate_ac10 / 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: 벤시몽 RAIN BOOTS MID - 7color DOLPHIN GREY_40 260 오리상점
	- text: 플레이볼 오리진 뮬 (PLAYBALL ORIGIN MULE) NY (Off White) 화이트_230 주식회사 에프앤에프
	- text: XDMNBTX0037 빅 사이즈 봄여름 블로퍼 고양이 액체설 블랙_265 푸른바다
	- text: 다이어트 슬리퍼 다리 부종 스트레칭 균형 실내화 핑크 33-37_33 글로벌다이렉트
	- text: 케즈 챔피온 스트랩 캔버스5 M01778F001 Black/Black/Black_230 블루빌리
	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: 0.6511206701381028
	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: 10 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 \|
	\|:------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 9.0 \| <ul><li>'로저비비에 로저 비비어 i 러브 비비어 슬링백 펌프스 RVW53834670PE5 여성 37 주식회사 페칭'</li><li>'크롬베즈 스티치 장식 통굽펌프스 KP55797MA 카멜／245 sellerhub'</li><li>'HOBOKEN PS1511 PH2208 (3컬러) 브라운 230 NC_백화점'</li></ul> \|
	\| 2.0 \| <ul><li>'어그클래식울트라미니 ugg 어그부츠 여성 방한화 여자 발편한 겨울 신발 1116109 Sage Blossom_US 6(230) 울바이울'</li><li>'해외문스타 810s ET027 마르케 모디 운동화 장화 레인부츠 일본 직구 300_코요테_모디ET027 뉴저지홀세일'</li><li>'무릎 위에 앉다 장화 롱부츠 굽이 거칠다 평평한 바닥 고통 라이더 부츠 블랙_225 ZHANG YOUHUA'</li></ul> \|
	\| 0.0 \| <ul><li>'단화 한복신발 여성 새 혼례 소프트 한복구두 전통 꽃신 자수 39_빅화이트백봉이는한사이즈크게찍으셨으면좋겠습 대복컴퍼니'</li><li>'한복구두 꽃신 양단 생활한복 키높이 단화 굽 빅사이즈 담그어 여름 터지는 구슬 화이트-3.5cm_41 대한민국 일등 상점'</li><li>'여자 키높이 신발 여성 꽃신 한복 구두 전통 계량한복 37_화이트12(지연) 유럽걸스'</li></ul> \|
	\| 4.0 \| <ul><li>'남여공용 청키 클로그 바운서 샌들 (3ASDCBC33) 블랙(50BKS)_240 '</li><li>'[포멜카멜레]쥬얼장식트위드샌들 3cm FJS1F1SS024 아이보리/255 에이케이에스앤디(주) AK플라자 평택점'</li><li>'[하프클럽/] 에끌라 투웨이 주얼 샌들 33.카멜/245mm 롯데아이몰'</li></ul> \|
	\| 8.0 \| <ul><li>'에스콰이아 여성 발편한 경량 세미 캐주얼 앵클 워커 부츠 3cm J278C 브라운_230 (주) 패션플러스'</li><li>'[제옥스](신세계강남점) 스페리카 EC7 여성 워커부츠-블랙 W1B6VDJ3W11 블랙_245(38) 주식회사 에스에스지닷컴'</li><li>'(신세계강남점)금강 랜드로바 경량 컴포트 여성 워커 부츠 LANBOC4107WK1 240 신세계백화점'</li></ul> \|
	\| 6.0 \| <ul><li>'10mm 2중바닥 실내 슬리퍼 병원 거실 호텔 실내화 슬리퍼-타올천_고급-C_검정 주식회사 하루이'</li><li>'소프달링 남녀공용 뽀글이 스마일 털슬리퍼 여성 겨울 털실내화 VJ/왕스마일/옐로우_255 소프달링'</li><li>'소프달링 남녀공용 뽀글이 스마일 털슬리퍼 여성 겨울 털실내화 VJ/왕스마일/옐로우_245 소프달링'</li></ul> \|
	\| 3.0 \| <ul><li>'지안비토로씨 여성 마고 미드 부티 GIA36T75BLU18A1A00 EU 38.5 봉쥬르유럽'</li><li>'모다아울렛 121507 여성 7cm 깔끔 스틸레토 부티 구두 블랙k040_250 ◈217326053◈ MODA아울렛'</li><li>'미들부츠 미들힐 봄신상 워커 롱부츠 봄 가을신상 힐 블랙 245 바이포비'</li></ul> \|
	\| 5.0 \| <ul><li>'[공식판매] 버켄스탁 지제 에바 EVA 블랙 화이트 07 비트루트퍼플 키즈_220 (34) 좁은발볼 (Narrow) '</li><li>'eva 털슬리퍼 방한 방수 따듯한 털신 통굽 실내 화 기모 크로스오버 블랙M 소보로샵'</li><li>'크록스호환내피 털 탈부착 퍼 겨울 슬리퍼 안감 크림화이트(주니어)_C10-165(155~165) 인터코리아'</li></ul> \|
	\| 7.0 \| <ul><li>'[밸롭] 구름 브리즈 베이지 구름 브리즈 베이지245 (주)지티에스글로벌'</li><li>'[스텝100] 무지외반증 허리디스크 평발 신발 무릎 관절 중년 여성 운동화 화이트핑크플라워_235 스텝100'</li><li>'물컹슈즈 2.0 기능성 운동화 발편한 쿠션 운동화 무지외반증신발 족저근막염 물컹 업그레이드2.0_네이비_46(280mm) 주식회사 나인투식스'</li></ul> \|
	\| 1.0 \| <ul><li>'베라왕 스타일온에어 23SS 청 플랫폼 로퍼 80111682 G 667381 틸블루_230 DM ENG'</li><li>'[MUJI] 발수 발이 편한 스니커 머스터드 235mm 4550182676303 무인양품(주)'</li><li>'[반스(슈즈)]반스 어센틱 체커보드 스니커즈 (VN000W4NDI0) 4.240 롯데아이몰'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Metric \|
	\|:--------\|:-------\|
	\| all \| 0.6511 \|

	## 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_ac10")
	# Run inference
	preds = model("XDMNBTX0037 빅 사이즈 봄여름 블로퍼 고양이 액체설 블랙_265 푸른바다")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 3 \| 10.504 \| 21 \|

	\| 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 \|
	\| 7.0 \| 50 \|
	\| 8.0 \| 50 \|
	\| 9.0 \| 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0127 \| 1 \| 0.4172 \| - \|
	\| 0.6329 \| 50 \| 0.3266 \| - \|
	\| 1.2658 \| 100 \| 0.1718 \| - \|
	\| 1.8987 \| 150 \| 0.095 \| - \|
	\| 2.5316 \| 200 \| 0.0257 \| - \|
	\| 3.1646 \| 250 \| 0.0142 \| - \|
	\| 3.7975 \| 300 \| 0.0026 \| - \|
	\| 4.4304 \| 350 \| 0.0164 \| - \|
	\| 5.0633 \| 400 \| 0.01 \| - \|
	\| 5.6962 \| 450 \| 0.0004 \| - \|
	\| 6.3291 \| 500 \| 0.0003 \| - \|
	\| 6.9620 \| 550 \| 0.0002 \| - \|
	\| 7.5949 \| 600 \| 0.0002 \| - \|
	\| 8.2278 \| 650 \| 0.0001 \| - \|
	\| 8.8608 \| 700 \| 0.0001 \| - \|
	\| 9.4937 \| 750 \| 0.0001 \| - \|
	\| 10.1266 \| 800 \| 0.0001 \| - \|
	\| 10.7595 \| 850 \| 0.0001 \| - \|
	\| 11.3924 \| 900 \| 0.0001 \| - \|
	\| 12.0253 \| 950 \| 0.0001 \| - \|
	\| 12.6582 \| 1000 \| 0.0001 \| - \|
	\| 13.2911 \| 1050 \| 0.0001 \| - \|
	\| 13.9241 \| 1100 \| 0.0001 \| - \|
	\| 14.5570 \| 1150 \| 0.0001 \| - \|
	\| 15.1899 \| 1200 \| 0.0001 \| - \|
	\| 15.8228 \| 1250 \| 0.0001 \| - \|
	\| 16.4557 \| 1300 \| 0.0001 \| - \|
	\| 17.0886 \| 1350 \| 0.0001 \| - \|
	\| 17.7215 \| 1400 \| 0.0001 \| - \|
	\| 18.3544 \| 1450 \| 0.0001 \| - \|
	\| 18.9873 \| 1500 \| 0.0001 \| - \|
	\| 19.6203 \| 1550 \| 0.0001 \| - \|

	### 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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