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	---
	base_model: mini1013/master_domain
	library_name: setfit
	metrics:
	- accuracy
	pipeline_tag: text-classification
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: '[당일출고] 한율 자연을 닮은 립밤 4g - 3호 옵션없음 제이에이치컴퍼니'
	- text: 릴리바이레드 러브빔 글로우 베일 3.2g 02 홀리빔 × 1개 옵션없음 원라이브브랜드
	- text: 에뛰드 컬픽스 마스카라 8g 그레이 브라운 버프샵
	- text: '[입생로랑] [리필] NEW 루쥬 쀠르 꾸뛰르 NM 뉘 뮤즈(리필) 엘오케이 (유)'
	- text: 우드버리 하드텍스처 아이브로우 펜슬 4g Timber Wolf 1개 1022244 옵션없음 배스테인
	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: accuracy
	value: 0.7551652892561983
	name: Accuracy
	---

	# 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: 13 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 \|
	\|:------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 1.0 \| <ul><li>'프로랑스 32호 입술펜슬 오토 립라이너 5W525AC824 옵션없음 주도매'</li><li>'로페스 오토 립라이너 펜슬 32호 옵션없음 북가무역'</li><li>'313764 BOBBI 브라운 립 라이너 COCOA 14 옵션없음 에스비 컴퍼니'</li></ul> \|
	\| 7.0 \| <ul><li>'에스티 로더 2024 홀리데이 블록버스터 세트 (11종 세트 & 파우치 + 홀리데이 쇼핑백 증정) 에스티 로더'</li><li>'에스쁘아 아이 코어 팔레트 9g + 톤페어링 리퀴드 치크 8ml, 2종 SET 에스쁘아 본사직영샵'</li><li>'[3개세트] 글래스팅 컬러 글로스 4g+글래스팅 멜팅 밤 3.5g+미니 쥬시 래스팅 틴트 2g 3종 세트 롬앤'</li></ul> \|
	\| 4.0 \| <ul><li>'[입생로랑] NEW 베르니 아 레브르 바이닐 크림 416 싸이키델릭 칠리 주식회사 인터파크커머스'</li><li>'맥 파우더 키스 리퀴드 립컬러 5ml 어 리틀 템드 옵션없음 PDValues LLC'</li><li>'베르니 아 레브르 바이닐 크림 8가지 색상 YSL610 주식회사 스타일가든이'</li></ul> \|
	\| 5.0 \| <ul><li>'페리페라 잉크 블랙 카라 풀볼륨 컬링 (주)금용주상사'</li><li>'(유통기한 임박)투쿨포스쿨 아트 클래스 매지컬 픽싱 마스카라 7g 2호 다크브라운(24.04까지) 리앤햇'</li><li>'데자뷰 파이버윅 울트라롱 마스카라 7.2g 내추럴브라운 피아스인터코스멕스한국 주식회사'</li></ul> \|
	\| 2.0 \| <ul><li>'페리페라 잉크 무드 매트 스틱 (색상선택) 06 모브병유발 (주)글로벌세림'</li><li>'[아워글래스](신세계 타임스퀘어점패션관)팬텀 볼류마이징 글로시 밤 트레이스 주식회사 에스에스지닷컴'</li><li>'입생로랑 루쥬 볼륍떼 캔디 글레이즈 3.2g 4 누드 플레져 주식회사 푸른거리'</li></ul> \|
	\| 12.0 \| <ul><li>'페리페라 잉크 브이 쉐딩 9.5g 2호카카오브라운 포라이프(4LIFE)'</li><li>'쉬글램 루나 글로우 하이라이터 1) Tea Cake 옐로우브릭로드'</li><li>'컬러그램 입체 창조 쉐딩 스틱 1.3g 3종택일 01호 웜톤 뷰테크'</li></ul> \|
	\| 0.0 \| <ul><li>'로라메르시에 립 그레이스 Mel테드 Sugar B 풀 사이즈 교수님선물 옵션없음 남인터내셔널'</li><li>'삐아오버글레이즈 삐아체리당 슈가시럽광 체리당 와우마트'</li><li>'맑은 색감 립 글로스 5g PK103 베이비핑크 옵션없음 송승찬'</li></ul> \|
	\| 9.0 \| <ul><li>'스머징 트임 아이라이너 0.12g (3colors) 1호 헤이즈 유럽피아'</li><li>'미샤 비비드 픽스 마커 펜 라이너 0.6g (딥브라운) 옵션없음 제이앤비'</li><li>'머지 더 퍼스트 슬림 젤 아이라이너 0.05g 옵션없음 다사다 유한책임회사'</li></ul> \|
	\| 8.0 \| <ul><li>'마샬리아 마이 온리 세럼 옵션없음 제이뷰티'</li><li>'(2+1) GEMSHO 젬소 더블이펙트 영양마스카라X2 옵션없음 주식회사 젬소(GEMSHO)'</li><li>'래쉬앤브로우 코팅에센스5ml 속눈썹 영양제 옵션없음 리얼뷰티'</li></ul> \|
	\| 3.0 \| <ul><li>'Burt’s Bees 홀리데이 선물 세트 옵션없음 샵인프랑'</li><li>'[라부르켓] 립 밤 아몬드/코코넛 14g 화이트_F (주)신세계인터내셔날'</li><li>'버츠비 비즈왁스 립밤 택4 종이케이스포함 종류_허니4개 제이마켓'</li></ul> \|
	\| 11.0 \| <ul><li>'미샤 트리플 섀도우 2g (레이디 밀크티) 옵션없음 제이앤비'</li><li>'노베브 언더 아이 마스터 0.8g 08호 코랄스칼렛 캐치헬스'</li><li>'맥 프로 롱웨어 페인트팟 아이섀도우 5g 부지 옵션없음 원라이브쇼핑'</li></ul> \|
	\| 6.0 \| <ul><li>'백화점정품) 크리니크 치크 팝 발레리나팝 3.5g 피치팝 제이 컴퍼니'</li><li>'누즈 무스 케어 치크 16ml 1021814 옵션없음 굿데이'</li><li>'힌스 트루 디멘션 래디언스 밤 10g(라이트) 옵션없음 옐로우로켓'</li></ul> \|
	\| 10.0 \| <ul><li>'바비브라운 퍼펙틀리 디파인드 롱웨어 브라우 펜슬 0.33g 옵션없음 안느의집'</li><li>'크리니크 브로우 쉐이퍼 3.1g 01 토우페 에르에르샵'</li><li>'맥 아이 브로우 스타일러 0.9g 1021649 페니 배스테인'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.7552 \|

	## 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_bt6_test")
	# Run inference
	preds = model("에뛰드 컬픽스 마스카라 8g 그레이 브라운 버프샵")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 4 \| 9.3296 \| 20 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0.0 \| 16 \|
	\| 1.0 \| 18 \|
	\| 2.0 \| 19 \|
	\| 3.0 \| 24 \|
	\| 4.0 \| 19 \|
	\| 5.0 \| 20 \|
	\| 6.0 \| 21 \|
	\| 7.0 \| 15 \|
	\| 8.0 \| 21 \|
	\| 9.0 \| 22 \|
	\| 10.0 \| 31 \|
	\| 11.0 \| 22 \|
	\| 12.0 \| 19 \|

	### Training Hyperparameters
	- batch_size: (512, 512)
	- num_epochs: (50, 50)
	- max_steps: -1
	- sampling_strategy: oversampling
	- num_iterations: 60
	- body_learning_rate: (2e-05, 1e-05)
	- head_learning_rate: 0.01
	- loss: CosineSimilarityLoss
	- distance_metric: cosine_distance
	- margin: 0.25
	- end_to_end: False
	- use_amp: False
	- warmup_proportion: 0.1
	- l2_weight: 0.01
	- seed: 42
	- eval_max_steps: -1
	- load_best_model_at_end: False

	### Training Results
	\| Epoch \| Step \| Training Loss \| Validation Loss \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0312 \| 1 \| 0.4833 \| - \|
	\| 1.5625 \| 50 \| 0.3686 \| - \|
	\| 3.125 \| 100 \| 0.0991 \| - \|
	\| 4.6875 \| 150 \| 0.0361 \| - \|
	\| 6.25 \| 200 \| 0.0224 \| - \|
	\| 7.8125 \| 250 \| 0.0132 \| - \|
	\| 9.375 \| 300 \| 0.0102 \| - \|
	\| 10.9375 \| 350 \| 0.0069 \| - \|
	\| 12.5 \| 400 \| 0.0012 \| - \|
	\| 14.0625 \| 450 \| 0.0002 \| - \|
	\| 15.625 \| 500 \| 0.0002 \| - \|
	\| 17.1875 \| 550 \| 0.0002 \| - \|
	\| 18.75 \| 600 \| 0.0001 \| - \|
	\| 20.3125 \| 650 \| 0.0001 \| - \|
	\| 21.875 \| 700 \| 0.0001 \| - \|
	\| 23.4375 \| 750 \| 0.0001 \| - \|
	\| 25.0 \| 800 \| 0.0001 \| - \|
	\| 26.5625 \| 850 \| 0.0001 \| - \|
	\| 28.125 \| 900 \| 0.0001 \| - \|
	\| 29.6875 \| 950 \| 0.0001 \| - \|
	\| 31.25 \| 1000 \| 0.0001 \| - \|
	\| 32.8125 \| 1050 \| 0.0001 \| - \|
	\| 34.375 \| 1100 \| 0.0001 \| - \|
	\| 35.9375 \| 1150 \| 0.0001 \| - \|
	\| 37.5 \| 1200 \| 0.0001 \| - \|
	\| 39.0625 \| 1250 \| 0.0001 \| - \|
	\| 40.625 \| 1300 \| 0.0001 \| - \|
	\| 42.1875 \| 1350 \| 0.0001 \| - \|
	\| 43.75 \| 1400 \| 0.0001 \| - \|
	\| 45.3125 \| 1450 \| 0.0001 \| - \|
	\| 46.875 \| 1500 \| 0.0001 \| - \|
	\| 48.4375 \| 1550 \| 0.0001 \| - \|
	\| 50.0 \| 1600 \| 0.0001 \| - \|

	### Framework Versions
	- Python: 3.10.12
	- SetFit: 1.1.0
	- Sentence Transformers: 3.3.1
	- Transformers: 4.44.2
	- PyTorch: 2.2.0a0+81ea7a4
	- Datasets: 3.2.0
	- Tokenizers: 0.19.1

	## 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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