master_cate_bt2 / README.md

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	---
	base_model: mini1013/setfit_robeta_base_s_domain
	library_name: setfit
	metrics:
	- metric
	pipeline_tag: text-classification
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: '[7매입/14매입] 마이크로바이옴 비건 모델링팩 모공 수축 수분 진정 마스크 팩 1set 1. 모델링팩 1set (7매입) 주식회사
	에이치티오인터내셔널'
	- text: 더후 공진향 인양 넥앤페이스 탄력 리페어75ml 옵션없음 씨플랩
	- text: 빌리프 슈퍼 나이츠-리제너레이팅 나이트 마스크 75ml 옵션없음 라임쇼핑
	- text: 수이스킨 편안한 진정초 시트 마스크 5개입 × 1개 민물유통
	- text: 참존 지안 극결 콘트롤 크림 225ml (리뉴얼제품) 옵션없음 슈슈
	inference: true
	model-index:
	- name: SetFit with mini1013/setfit_robeta_base_s_domain
	results:
	- task:
	type: text-classification
	name: Text Classification
	dataset:
	name: Unknown
	type: unknown
	split: test
	metrics:
	- type: metric
	value: 0.7714285714285715
	name: Metric
	---

	# SetFit with mini1013/setfit_robeta_base_s_domain

	This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [mini1013/setfit_robeta_base_s_domain](https://huggingface.co/mini1013/setfit_robeta_base_s_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/setfit_robeta_base_s_domain](https://huggingface.co/mini1013/setfit_robeta_base_s_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: 8 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>'메노킨 30초 퀵 버블 마스크 3종세트 (선물포장+세안밴드 ) 옵션없음 주식회사 포레스트에비뉴'</li><li>'라네즈 립 슬리핑 마스크EX 20g 각질케어 수면팩 자몽 20g (주)아모레퍼시픽'</li><li>'빌리프 슈퍼 나이츠 멀티 비타민 마스크 75ml 옵션없음 라임쇼핑'</li></ul> \|
	\| 5 \| <ul><li>'[러쉬]국내제조 컵 오 커피 325g - 페이스 마스크 / 커피팩 옵션없음 주식회사 러쉬코리아'</li><li>'올리고더미 딥클렌징 마스크 110ml+25ml+캐릭터파우치+스파츌라 리바이탈라이징 25ml(5mlx5매) [영양] 주식회사더마셀'</li><li>'스킨푸드 라벤더 푸드마스크 120g 02 딸기슈가_1개 미루코스메틱'</li></ul> \|
	\| 3 \| <ul><li>'[에스테프로] 708 티트리 알게러버 2000ml 고무마스크 모델링팩 피부미용사 실기준비물 에스테맥스 옵션없음 무제'</li><li>'린제이 프리미엄 쿨 티트리 모델링 마스크 고무팩 820g 11203606 옵션없음 세론세론'</li><li>'메디플라워 대용량 네이처 허브 모델링팩 쿨티트리 500g+히알루론산 멀티 부스터 500ml 쿨 티트리 500g_히알루론산 멀티 부스터 (주)메디플라워'</li></ul> \|
	\| 2 \| <ul><li>'바이오던스 바이오 콜라겐 리얼 딥 마스크 1매 옵션없음 에스제이유통'</li><li>'아리얼 세븐데이즈 마스크 카렌듈라 P 1매 도매가능 옵션없음 앱스'</li><li>'바노바기 밀크 씨슬 리페어 시카 퀵 마스크 플러스(30매) 제품 구매 시 +시카 폼 미니어처 1EA 증정 주식회사 바노바기'</li></ul> \|
	\| 6 \| <ul><li>'포잇 카카두 허니씨 톤업패드 60매 3개 옵션없음 스타일리시케이'</li><li>'미팩토리 3단 돼지 코팩, 3개입, 6개 3개입 × 6개 제이케이컴퍼니'</li><li>'올가드 아웃도어 패치 2패치 4매 3개 옵션없음 스타일리시케이'</li></ul> \|
	\| 0 \| <ul><li>'오릭스마사지크림(450ml) 옵션없음 고호성'</li><li>'모든순간 살구씨 찌든때 비타민 피지 살구스크럽 500g 옵션없음 데이포유'</li><li>'필로티카 시트랑스 겔 마스크 270ml 필로티카 시트랑스 겔 마스크 270ml 오드엘르'</li></ul> \|
	\| 1 \| <ul><li>'코 숨쉬기 입벌림 방지 호흡 유도 수면 보조 밴드 코골이 기구 용품 개선 제품 완화 기기 옵션없음 구씨네유통'</li><li>'희재감성 라인정리 이중턱 밴드 관리 리프팅 안면윤곽 단품(1701) 희재감성몰'</li><li>'50 개/몫 화이트 판지 카드 상자 포장 빈 마스크 더블 오픈 01 WHITE_06 9.2x8x5cm-50pcs 글로젠'</li></ul> \|
	\| 7 \| <ul><li>'ECLADO 에끌라두 로얄 밀크 프로틴 페이셜 마스크 70g 단백질 필오프팩 1개 옵션없음 주식회사 아워스'</li><li>'비건이펙트 슬로우 앤 에이징 저분자 콜라겐 물광 랩마스크 80ml 옵션없음 (주)부스트랩'</li><li>'메디필 레드락토 콜라겐 랩핑마스크 70ml 물광 리프팅 팩 옵션없음 웬디스룸'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Metric \|
	\|:--------\|:-------\|
	\| all \| 0.7714 \|

	## 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_bt2")
	# Run inference
	preds = model("수이스킨 편안한 진정초 시트 마스크 5개입 × 1개 민물유통")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 3 \| 9.8591 \| 27 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0 \| 90 \|
	\| 1 \| 78 \|
	\| 2 \| 88 \|
	\| 3 \| 95 \|
	\| 4 \| 94 \|
	\| 5 \| 90 \|
	\| 6 \| 84 \|
	\| 7 \| 34 \|

	### Training Hyperparameters
	- batch_size: (512, 512)
	- num_epochs: (20, 20)
	- max_steps: -1
	- sampling_strategy: oversampling
	- num_iterations: 30
	- 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0130 \| 1 \| 0.4922 \| - \|
	\| 0.6494 \| 50 \| 0.2317 \| - \|
	\| 1.2987 \| 100 \| 0.0726 \| - \|
	\| 1.9481 \| 150 \| 0.033 \| - \|
	\| 2.5974 \| 200 \| 0.0322 \| - \|
	\| 3.2468 \| 250 \| 0.0056 \| - \|
	\| 3.8961 \| 300 \| 0.001 \| - \|
	\| 4.5455 \| 350 \| 0.0003 \| - \|
	\| 5.1948 \| 400 \| 0.0001 \| - \|
	\| 5.8442 \| 450 \| 0.0001 \| - \|
	\| 6.4935 \| 500 \| 0.0001 \| - \|
	\| 7.1429 \| 550 \| 0.0002 \| - \|
	\| 7.7922 \| 600 \| 0.0001 \| - \|
	\| 8.4416 \| 650 \| 0.0001 \| - \|
	\| 9.0909 \| 700 \| 0.0001 \| - \|
	\| 9.7403 \| 750 \| 0.0001 \| - \|
	\| 10.3896 \| 800 \| 0.0006 \| - \|
	\| 11.0390 \| 850 \| 0.0001 \| - \|
	\| 11.6883 \| 900 \| 0.0001 \| - \|
	\| 12.3377 \| 950 \| 0.0001 \| - \|
	\| 12.9870 \| 1000 \| 0.0 \| - \|
	\| 13.6364 \| 1050 \| 0.0 \| - \|
	\| 14.2857 \| 1100 \| 0.0 \| - \|
	\| 14.9351 \| 1150 \| 0.0 \| - \|
	\| 15.5844 \| 1200 \| 0.0 \| - \|
	\| 16.2338 \| 1250 \| 0.0 \| - \|
	\| 16.8831 \| 1300 \| 0.0 \| - \|
	\| 17.5325 \| 1350 \| 0.0 \| - \|
	\| 18.1818 \| 1400 \| 0.0 \| - \|
	\| 18.8312 \| 1450 \| 0.0 \| - \|
	\| 19.4805 \| 1500 \| 0.0 \| - \|

	### Framework Versions
	- Python: 3.10.12
	- SetFit: 1.1.0.dev0
	- Sentence Transformers: 3.1.1
	- Transformers: 4.45.1
	- 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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