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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: 진주 선 마린 스프레이 180ml 펄 제이엠솔루션 청광 옵션없음 사구있오
	- text: 핑크 레오파드 호피무늬 태닝비키니 바디프로필 몸매 - 표범 표범_M 세일러7
	- text: 제이엠솔루션 180ml 스프레이 선 진주 마린 펄 청광 옵션없음 뿔샵
	- text: 제주온 큐테라 풋귤 알로에베라 수딩젤 200ml × 1개 구대연구소
	- text: BALIBODY SPF 6 카카오 태닝 오일 100ml x 5개 옵션없음 시연마켓
	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: 0.7882599580712788
	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: 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>'디자이너스킨 올 액세스 태닝로션 여름 크림 옵션없음 라이브프롬잇'</li><li>'코스노리 올웨이즈 핏 바 비키니 코드 바디젤 263003 J 옵션없음 제이피샵온(JPshopon)'</li><li>'Bronzer Tanning Lotion by 디자이너스킨 400ml 옵션없음 라이브프롬잇'</li></ul> \|
	\| 5.0 \| <ul><li>'썬범 프리미엄 하이드레이팅 애프터 썬 젤 쿨다운 237ml - 썬 젤 쿨다운 237ml x 2개_없음 나대몰'</li><li>'Allurials 알로에 베라 젤 354ml 옵션없음 해외쇼핑 잘왔네'</li><li>'웰더마 지플러스 쿨링 에센스 알로에베라 수딩젤 120g × 3개 120g × 3개 하이블리스'</li></ul> \|
	\| 1.0 \| <ul><li>'SVR 선시큐어 울트라 라이트 인비저블 선 스프레이 SPF50 200ml 옵션없음 주식회사하늘'</li><li>'선스프레이 청광 진주 마린 제이엠솔루션 선 스프레이 펄 옵션없음 유토피아'</li><li>'청광 선 180ml 스프레이 제이엠솔루션 마린 진주 펄 옵션없음 포뿔샵'</li></ul> \|
	\| 0.0 \| <ul><li>'큐어 쿨링 선스틱 23g 2개 옵션없음 씨유니'</li><li>'라운드랩 자작나무 수분 선스틱 19g SPF 50+ 자작 수분 선스틱 19g 원스원컴퍼니'</li><li>'AHC 내추럴 퍼펙션 더블 쉴드 선스틱 (파랑)14g 골프 등산 워터프루프 썬크림 옵션없음 위얼드(WEALD)'</li></ul> \|
	\| 4.0 \| <ul><li>'엘로엘 2024 시즌8 팡팡 빅선쿠션 S8 스마일썬쿠션 본품 25g 옵션없음 더블아이'</li><li>'엘로엘 팡팡 빅 선쿠션 시즌 8 본품25g + 리필25g 옵션없음 미소샵'</li><li>'BRTC 마일드 선쿠션 본품+리필 기획 (디즈니) 옵션없음 지구상사'</li></ul> \|
	\| 3.0 \| <ul><li>'듀이셀 필터링크림 40ml (SPF50+) 옵션없음 오션컴퍼니'</li><li>'아쿠아선크림40ml+미니폼40ml / 24시간촉촉 비건 무기자차 SPF50+ PA++++ 옵션없음 에프엔지뷰티랩 주식회사'</li><li>'알리코제약 이나벨로 유기,무기자차 혼합자차 톤업 선크림 옵션없음 알리코제약(주)'</li></ul> \|
	\| 2.0 \| <ul><li>'리쥬란 바이옴 힐러 선케어 2종 세트(바이옴 힐러 선 크림 50mL SPF50+ + 바이옴 힐러 선 밤 19g SPF50+) 리쥬란 힐러'</li><li>'라로제 클린 선스틱 SPF50 18.5g+수분스틱 15ml 세트 라로제 코스메틱'</li><li>'[로우퀘스트] 베리어 인핸싱 선크림(SPF50+PA++++) + 에키네시아 선스틱(SPF50+PA++++) 2종 세트 로우퀘스트'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.7883 \|

	## 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_bt7_test")
	# Run inference
	preds = model("제주온 큐테라 풋귤 알로에베라 수딩젤 200ml × 1개 구대연구소")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:--------\|:----\|
	\| Word count \| 4 \| 10.1504 \| 24 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0.0 \| 20 \|
	\| 1.0 \| 10 \|
	\| 2.0 \| 17 \|
	\| 3.0 \| 28 \|
	\| 4.0 \| 20 \|
	\| 5.0 \| 15 \|
	\| 6.0 \| 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 \|
	\|:------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0625 \| 1 \| 0.4924 \| - \|
	\| 3.125 \| 50 \| 0.291 \| - \|
	\| 6.25 \| 100 \| 0.0536 \| - \|
	\| 9.375 \| 150 \| 0.0011 \| - \|
	\| 12.5 \| 200 \| 0.0002 \| - \|
	\| 15.625 \| 250 \| 0.0001 \| - \|
	\| 18.75 \| 300 \| 0.0001 \| - \|
	\| 21.875 \| 350 \| 0.0001 \| - \|
	\| 25.0 \| 400 \| 0.0001 \| - \|
	\| 28.125 \| 450 \| 0.0001 \| - \|
	\| 31.25 \| 500 \| 0.0001 \| - \|
	\| 34.375 \| 550 \| 0.0001 \| - \|
	\| 37.5 \| 600 \| 0.0001 \| - \|
	\| 40.625 \| 650 \| 0.0001 \| - \|
	\| 43.75 \| 700 \| 0.0001 \| - \|
	\| 46.875 \| 750 \| 0.0001 \| - \|
	\| 50.0 \| 800 \| 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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