master_cate_fi10 / README.md

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	---
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: 미드센추리 투명 아크릴 스테인리스 트롤리 이동식 거실 테이블 가구/인테리어>주방가구>왜건/카트
	- text: 복고풍 황동철제 에메랄드 인테리어 리빙 고급 대리석 사각테이블 가구/인테리어>주방가구>식탁/의자>식탁테이블
	- text: 스칸디아 우디 800 1200 반타원형 수납 원목 테이블 식탁 착불배송 가구/인테리어>주방가구>식탁/의자>식탁테이블
	- text: 퍼니코 어반 라미네이트 반타원 1200 4인용 식탁 세트 식탁 의자2P 벤치1P 1000 벤치 포인트체어 1200X800 가구/인테리어>주방가구>식탁/의자>식탁세트
	- text: 모던 홈 바 테이블 세트 아일랜드 식탁 100 240cm-길이 200 폭 30 높이 100 가구/인테리어>주방가구>식탁/의자>아일랜드식탁
	metrics:
	- accuracy
	pipeline_tag: text-classification
	library_name: setfit
	inference: true
	base_model: mini1013/master_domain
	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: 1.0
	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: 6 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 \|
	\|:------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 2.0 \| <ul><li>'밥솥다이 전자렌지선반 광파오븐장 1200 라빈화이트 가구/인테리어>주방가구>레인지대'</li><li>'가구느낌 전자레인지 수납장 4단 밥솥 다이 렌지 선반 가구/인테리어>주방가구>레인지대'</li><li>'가구레시피 한정이벤트 조립식 시그니처 진열장형 5단 선반장 렌지대 주방수납장 밥솥다이 가구/인테리어>주방가구>레인지대'</li></ul> \|
	\| 0.0 \| <ul><li>'장미맨숀 마르틴 원목 그릇장 가구/인테리어>주방가구>그릇장/컵보드'</li><li>'찻잔 장식장 다기 진열 홈카페 수납장 주방 선반 그 -17 오동나무 12칸 벽걸이형 가구/인테리어>주방가구>그릇장/컵보드'</li><li>'찬장 원목 그릇장 빈티지 주방 수납장 엔틱 미닫이 진열장 가구/인테리어>주방가구>그릇장/컵보드'</li></ul> \|
	\| 5.0 \| <ul><li>'아이엔지홈 킨포크 주방수납장 1200 가구/인테리어>주방가구>주방수납장'</li><li>'리바트키친 트루 주방 수납장 가구/인테리어>주방가구>주방수납장'</li><li>'화이트 수납 캐비닛 주방 지중해 갤러리 찬장 가구/인테리어>주방가구>주방수납장'</li></ul> \|
	\| 4.0 \| <ul><li>'이동식 트롤리 바퀴달린 리어카 선반 다이닝카 다층선반 미드센추리 가구/인테리어>주방가구>왜건/카트'</li><li>'진료 선반 병원 카트 치과 트레이 드레싱 장비 수납 가구/인테리어>주방가구>왜건/카트'</li><li>'밀스턴 튼튼한 이동식 트롤리 3단 가구/인테리어>주방가구>왜건/카트'</li></ul> \|
	\| 3.0 \| <ul><li>'화이트 엣지 600 원형 18T 라운딩 테이블 가구/인테리어>주방가구>식탁/의자>식탁테이블'</li><li>'600x2000 키큰 주방 렌지대 겸 접이식 식탁 밥솥 다이 가구/인테리어>주방가구>식탁/의자>레인지대겸용식탁'</li><li>'웰퍼니쳐 클로이 고무나무 원목 6인 식탁세트 의자6 가구/인테리어>주방가구>식탁/의자>식탁세트'</li></ul> \|
	\| 1.0 \| <ul><li>'흡수가 빠른 씽크대선반건조대 규조토드라잉매트 가구/인테리어>주방가구>기타주방가구'</li><li>'업소용 싱크대 영업용 식당 스텐 주방 씽크대 개수대 가구/인테리어>주방가구>기타주방가구'</li><li>'주방 식당 스텐 배수 조리대 작업대 테이블 싱크대 업소용 스테인레스 가구/인테리어>주방가구>기타주방가구'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 1.0 \|

	## 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_fi10")
	# Run inference
	preds = model("미드센추리 투명 아크릴 스테인리스 트롤리 이동식 거실 테이블 가구/인테리어>주방가구>왜건/카트")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 3 \| 9.0476 \| 15 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0.0 \| 70 \|
	\| 1.0 \| 70 \|
	\| 2.0 \| 70 \|
	\| 3.0 \| 70 \|
	\| 4.0 \| 70 \|
	\| 5.0 \| 70 \|

	### Training Hyperparameters
	- batch_size: (256, 256)
	- num_epochs: (30, 30)
	- max_steps: -1
	- sampling_strategy: oversampling
	- num_iterations: 50
	- 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0120 \| 1 \| 0.494 \| - \|
	\| 0.6024 \| 50 \| 0.4972 \| - \|
	\| 1.2048 \| 100 \| 0.4906 \| - \|
	\| 1.8072 \| 150 \| 0.1734 \| - \|
	\| 2.4096 \| 200 \| 0.0195 \| - \|
	\| 3.0120 \| 250 \| 0.0002 \| - \|
	\| 3.6145 \| 300 \| 0.0 \| - \|
	\| 4.2169 \| 350 \| 0.0 \| - \|
	\| 4.8193 \| 400 \| 0.0001 \| - \|
	\| 5.4217 \| 450 \| 0.0 \| - \|
	\| 6.0241 \| 500 \| 0.0 \| - \|
	\| 6.6265 \| 550 \| 0.0 \| - \|
	\| 7.2289 \| 600 \| 0.0 \| - \|
	\| 7.8313 \| 650 \| 0.0 \| - \|
	\| 8.4337 \| 700 \| 0.0 \| - \|
	\| 9.0361 \| 750 \| 0.0 \| - \|
	\| 9.6386 \| 800 \| 0.0 \| - \|
	\| 10.2410 \| 850 \| 0.0 \| - \|
	\| 10.8434 \| 900 \| 0.0 \| - \|
	\| 11.4458 \| 950 \| 0.0 \| - \|
	\| 12.0482 \| 1000 \| 0.0 \| - \|
	\| 12.6506 \| 1050 \| 0.0 \| - \|
	\| 13.2530 \| 1100 \| 0.0 \| - \|
	\| 13.8554 \| 1150 \| 0.0 \| - \|
	\| 14.4578 \| 1200 \| 0.0 \| - \|
	\| 15.0602 \| 1250 \| 0.0 \| - \|
	\| 15.6627 \| 1300 \| 0.0 \| - \|
	\| 16.2651 \| 1350 \| 0.0 \| - \|
	\| 16.8675 \| 1400 \| 0.0 \| - \|
	\| 17.4699 \| 1450 \| 0.0 \| - \|
	\| 18.0723 \| 1500 \| 0.0 \| - \|
	\| 18.6747 \| 1550 \| 0.0 \| - \|
	\| 19.2771 \| 1600 \| 0.0 \| - \|
	\| 19.8795 \| 1650 \| 0.0 \| - \|
	\| 20.4819 \| 1700 \| 0.0 \| - \|
	\| 21.0843 \| 1750 \| 0.0 \| - \|
	\| 21.6867 \| 1800 \| 0.0 \| - \|
	\| 22.2892 \| 1850 \| 0.0 \| - \|
	\| 22.8916 \| 1900 \| 0.0 \| - \|
	\| 23.4940 \| 1950 \| 0.0 \| - \|
	\| 24.0964 \| 2000 \| 0.0 \| - \|
	\| 24.6988 \| 2050 \| 0.0 \| - \|
	\| 25.3012 \| 2100 \| 0.0 \| - \|
	\| 25.9036 \| 2150 \| 0.0 \| - \|
	\| 26.5060 \| 2200 \| 0.0 \| - \|
	\| 27.1084 \| 2250 \| 0.0 \| - \|
	\| 27.7108 \| 2300 \| 0.0 \| - \|
	\| 28.3133 \| 2350 \| 0.0 \| - \|
	\| 28.9157 \| 2400 \| 0.0 \| - \|
	\| 29.5181 \| 2450 \| 0.0 \| - \|

	### 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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