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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: '[피부과전용]리쥬란 힐링 아이젤 15ml +샘플키트+마스크팩 옵션없음 더마초이스'
	- text: 참존 탑클래스 리프팅 스킨 120ml 옵션없음 하루뷰티
	- text: Is Clinical 이즈클리니컬 퍼밍 콤플렉스 50ml/1.7oz 옵션없음 타임투글로벌
	- text: 데저트 에센스 퓨어 호호바오일 118ml 59ml+59ml 우성글로벌
	- text: AHC 누드 톤업 크림 내추럴 글로우 40ml+컨실링스틱 3.5g 2개 O1B_01)크림40ml+스틱3.5g2개 (주)카버코리아
	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: 0.7988636363636363
	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: 11 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>'스위스유스트 노간주 크림 100ml 옵션없음 무한상사'</li><li>'퓨어라이트 아로니아베리 주름개선 크림 옵션없음 퓨어 리테일'</li><li>'내추럴더마 프로젝트 시카 바이오 플라센타 크림 50ml 1021828 시카 바이오 플라센타 크림 1개 앤디월드'</li></ul> \|
	\| 1.0 \| <ul><li>'네오팜 제로이드 인텐시브 로션 MD 200ml 옵션없음 옐로우로켓'</li><li>'[케이스훼손] 더 후 공진향 인양 로션 110ml (케이스훼손) 인양 로션. 주식회사 포러스'</li><li>'크리니크 드라마티컬리 디퍼런트 모이스처라이징 젤 125ml(건성, 중복합) 옵션없음 옐로우로켓'</li></ul> \|
	\| 10.0 \| <ul><li>'하다라보 고쿠쥰 2종 세트(신형) 옵션없음 오레오'</li><li>'코스트코 프리메라 오가니언스 베리어 리페어 2종 선물세트 옵션없음 하랑'</li><li>'인셀덤 더마톨로지 퍼스트패키지 EX 옵션없음 보니따 주식회사'</li></ul> \|
	\| 7.0 \| <ul><li>'스킨푸드 캐롯 카로틴 카밍 워터 패드 60매 당근패드 닦토 11203452 캐롯 카로틴 카밍 워터 패드 30매 리필 캐롯 카로틴 모이스트 이펙터 52ml 메이써니'</li><li>'스킨푸드 미나리 패드 토너 닥토 닦토 60매 옵션없음 찬이네마켓'</li><li>'[리얼베리어] 익스트림 판테놀 토너 패드 150ml(80매) 옵션없음 (주)네오팜'</li></ul> \|
	\| 4.0 \| <ul><li>'Double Serum Eye 20ml 클라랑스 옵션없음 팬더샐러'</li><li>'가히 아이밤 9g 옵션없음 엠컴퍼니'</li><li>'마티나겝하르트 아보카도 아이크림 15ml [공식수입원] 옵션없음 (주)그레이스클럽'</li></ul> \|
	\| 9.0 \| <ul><li>'스킨아이 유기농 티트리 오일 옵션없음 폴슨 주식회사(FOLSN Inc.)'</li><li>'티트리 오일 10ML_리뉴얼 옵션없음 (주)씨제이이엔엠'</li><li>'스킨아이 유기농 티트리 오일 10ml 옵션없음 다사다 유한책임회사'</li></ul> \|
	\| 0.0 \| <ul><li>'가로목주름 세로목주름 제거 목탄력 턱살크림 우주를 담아'</li><li>'[1+1] 목주름 다리미 넥스틱 펩타이드 리들 1000샷 탄력 리프팅 안티에이징 옵션없음 소중한 우리아기'</li><li>'Sisley 시슬리 젠틀 브러쉬 포 페이스 앤 넥 1pcs 옵션없음 타임투글로벌'</li></ul> \|
	\| 8.0 \| <ul><li>'AHC 누드톤업크림 내추럴글로우 40ml 옵션없음 가온'</li><li>'내추럴더마 프로젝트 비타민 B9 톤업 미백 크림 65ml 옵션없음 음파 유한책임회사'</li><li>'롬앤 백미 톤업 크림 세트 백미 크림 2개 이엘엠(ELM)'</li></ul> \|
	\| 2.0 \| <ul><li>'자트인사이트 울트라 셋팅 진짜 픽서 50ml 옵션없음 49행운스토어'</li><li>'ECLADO NK-CX 프로틴 포텐 부스터 세포코팅 미스트 뿌리는 단백질 정품 에끌라두 NK-CX 프로틴 포텐 부스터 하이그래'</li><li>'최대 43% / 달바 옐로우 미스트 세럼 100mlX2개 [교차] 옐로우100ml+레드100ml 주식회사 달바글로벌'</li></ul> \|
	\| 3.0 \| <ul><li>'톤28 pH 균형제 토너 250ml × 1개 100ml x 2개 마켓 헤이데이'</li><li>'세레코 토리든 다이브인 저분자 히알루론산 토너 300ml(지성, 복합) 옵션없음 옐로우로켓'</li><li>'톤28 플러스마이너스 pH 균형제 (토너)250ml 옵션없음 퍼펙트픽스'</li></ul> \|
	\| 5.0 \| <ul><li>'CHANEL 르 리프트 프로 컨센트레이트 컨투어 50 ml 하이그래'</li><li>'브링그린 알로에 99% 수딩 젤 300ml(민감성)/JL 옵션없음 주식회사 제이엘'</li><li>'[설화수] 윤조 에센스 6세대 90mlss 옵션없음 빈티지브릿지'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.7989 \|

	## 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_bt8_test")
	# Run inference
	preds = model("참존 탑클래스 리프팅 스킨 120ml 옵션없음 하루뷰티")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 4 \| 9.2179 \| 23 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0.0 \| 18 \|
	\| 1.0 \| 18 \|
	\| 2.0 \| 22 \|
	\| 3.0 \| 20 \|
	\| 4.0 \| 32 \|
	\| 5.0 \| 30 \|
	\| 6.0 \| 40 \|
	\| 7.0 \| 23 \|
	\| 8.0 \| 17 \|
	\| 9.0 \| 14 \|
	\| 10.0 \| 23 \|

	### 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0323 \| 1 \| 0.4874 \| - \|
	\| 1.6129 \| 50 \| 0.3751 \| - \|
	\| 3.2258 \| 100 \| 0.0862 \| - \|
	\| 4.8387 \| 150 \| 0.0251 \| - \|
	\| 6.4516 \| 200 \| 0.0101 \| - \|
	\| 8.0645 \| 250 \| 0.0042 \| - \|
	\| 9.6774 \| 300 \| 0.0045 \| - \|
	\| 11.2903 \| 350 \| 0.0044 \| - \|
	\| 12.9032 \| 400 \| 0.0041 \| - \|
	\| 14.5161 \| 450 \| 0.0043 \| - \|
	\| 16.1290 \| 500 \| 0.0042 \| - \|
	\| 17.7419 \| 550 \| 0.0042 \| - \|
	\| 19.3548 \| 600 \| 0.004 \| - \|
	\| 20.9677 \| 650 \| 0.0043 \| - \|
	\| 22.5806 \| 700 \| 0.0042 \| - \|
	\| 24.1935 \| 750 \| 0.004 \| - \|
	\| 25.8065 \| 800 \| 0.0004 \| - \|
	\| 27.4194 \| 850 \| 0.0001 \| - \|
	\| 29.0323 \| 900 \| 0.0001 \| - \|
	\| 30.6452 \| 950 \| 0.0001 \| - \|
	\| 32.2581 \| 1000 \| 0.0001 \| - \|
	\| 33.8710 \| 1050 \| 0.0001 \| - \|
	\| 35.4839 \| 1100 \| 0.0001 \| - \|
	\| 37.0968 \| 1150 \| 0.0001 \| - \|
	\| 38.7097 \| 1200 \| 0.0001 \| - \|
	\| 40.3226 \| 1250 \| 0.0001 \| - \|
	\| 41.9355 \| 1300 \| 0.0001 \| - \|
	\| 43.5484 \| 1350 \| 0.0001 \| - \|
	\| 45.1613 \| 1400 \| 0.0001 \| - \|
	\| 46.7742 \| 1450 \| 0.0001 \| - \|
	\| 48.3871 \| 1500 \| 0.0001 \| - \|
	\| 50.0 \| 1550 \| 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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