master_cate_fi5 / README.md

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	---
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: 아레나 모던 대리석 다용도 도어 수납장 가구/인테리어>수납가구>수납장
	- text: 원목 문갑 약장 자개농 엔틱 고가구 거실 인테리어 선반 가구/인테리어>수납가구>고가구
	- text: 아카시아 대용량 2단 선반 행거 400호 가구/인테리어>수납가구>행거
	- text: 수납 박스 우드 원목 상자 케이스 나무함 정리함 바늘 실 가구/인테리어>수납가구>소품수납함
	- text: 엽서 진열대 전단지 전시대 디스플레이 회전 가판대 매거진 가구/인테리어>수납가구>잡지꽂이
	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: 12 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 \|
	\|:------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 10.0 \| <ul><li>'멜리사 거울 수납장 600 가구/인테리어>수납가구>코너장'</li><li>'아도라퍼니처 아동가구 모듈 4단 코너장 가구/인테리어>수납가구>코너장'</li><li>'거실 코너장 틈새 수납 수납장 모서리 장식 원목 삼각 가구/인테리어>수납가구>코너장'</li></ul> \|
	\| 0.0 \| <ul><li>'원목 LP 수납장 우드 레코드판 진열장 뉴송 정리 가구/인테리어>수납가구>CD/DVD장'</li><li>'원목 아크릴 레코드 진열대 랙 보관함 투명 lp정리함 가구/인테리어>수납가구>CD/DVD장'</li><li>'턴테이블 장식장 엘피장 LP 원목진열대 받침대 보관함 가구/인테리어>수납가구>CD/DVD장'</li></ul> \|
	\| 3.0 \| <ul><li>'PD몰 주물장식 나비 대 4번 주물자재 문종소품 가구/인테리어>수납가구>나비장'</li><li>'나비장 원목 앤틱 수납 장식장 가구/인테리어>수납가구>나비장'</li><li>'그라데이션조각컷팅 홈퍼니싱매장데코소품 이중겹날개나비장식 6p 가구/인테리어>수납가구>나비장'</li></ul> \|
	\| 11.0 \| <ul><li>'행거맨 옷 강력흡착 압축기 모기장 매장용 물밀대 옷걸이 시스템 조립식 미니 먼지털이 벽 가구/인테리어>수납가구>행거'</li><li>'행거 옷걸이 북유럽 옷걸이 원목 스탠드 미니 스탠딩 나무 행거 -17 나뭇가지 가구/인테리어>수납가구>행거'</li><li>'심플한 스탠행거 사무실 욕실 미니행거 가구/인테리어>수납가구>행거'</li></ul> \|
	\| 4.0 \| <ul><li>'팬트리선반 5단 펜트리 철제 다용도실 조립 시스템 베란다 가구/인테리어>수납가구>선반'</li><li>'마켓비 LEITER 선반 600 넓은 4단 가구/인테리어>수납가구>선반'</li><li>'원목 선반 베란다 정리 우드 랙 다용도 진열대 심플 모듈 가구/인테리어>수납가구>선반'</li></ul> \|
	\| 2.0 \| <ul><li>'아이리스 펠리칸 도어형 공간박스 STB-400D 가구/인테리어>수납가구>공간박스'</li><li>'온라인박스 택배박스 B골 66호 가구/인테리어>수납가구>공간박스'</li><li>'심스하임 400 공간박스 4단 24t W컷 안전 아이방 틈새 수납 미니 책장 꽂이 완제품 가구/인테리어>수납가구>공간박스'</li></ul> \|
	\| 7.0 \| <ul><li>'신발걸이 현관 벤치 북유럽 그물 빨간 신발장 가구/인테리어>수납가구>신발장'</li><li>'대용량 이동식 신발장 학원 기숙사 현관문 공부방 가구/인테리어>수납가구>신발장'</li><li>'현관 신발장 슬라이딩 초박형 학원 북유럽 붙박이 BD 가구/인테리어>수납가구>신발장'</li></ul> \|
	\| 8.0 \| <ul><li>'우산 비닐 포장기 정리 봉투 보관 홀더 꽂이 업소 자동 혼합형 사무실 통 대용량 입구 빗물 가구/인테리어>수납가구>우산꽂이'</li><li>'설렘하우스 개업선물 큰우산 우산꽂이 특대 사이즈 업소용 설렘하우스 가구/인테리어>수납가구>우산꽂이'</li><li>'반투명 우산파우치 봉투 비닐 가구/인테리어>수납가구>우산꽂이'</li></ul> \|
	\| 6.0 \| <ul><li>'Fusion 접이식수납장 다용도 이동식 다층 접이식 리빙 박스 무설치 양문 가정용 거실 수납함 가구/인테리어>수납가구>수납장'</li><li>'제인2 미니수납장 작은 수납장 원룸 미닫이 슬라이드 슬라이딩 수납장 600 일반형 가구/인테리어>수납가구>수납장'</li><li>'JD홈드레싱 3단 도어장 가구/인테리어>수납가구>수납장'</li></ul> \|
	\| 5.0 \| <ul><li>'Ac B8 청색 세로형 산다케이스 군 다산 64X91 가구/인테리어>수납가구>소품수납함'</li><li>'아트사인 원목 수납정리함 5칸 소품 정리 박스 미니 보관함 통 가구/인테리어>수납가구>소품수납함'</li><li>'플라스틱 헤라 가구/인테리어>수납가구>소품수납함'</li></ul> \|
	\| 1.0 \| <ul><li>'약장 오동나무 고가구 원목 나비장 가구/인테리어>수납가구>고가구'</li><li>'나전칠기 약장 원목 자개장 화초장 럭셔리 장식 캐비닛 가구/인테리어>수납가구>고가구'</li><li>'자개장 고가구 거실장 엔틱 빈티지 전통 가구/인테리어>수납가구>고가구'</li></ul> \|
	\| 9.0 \| <ul><li>'홍보책 잡지꽂이 책 진열대 잡지 매거진 팜플렛 거치대 선반 랙 가구/인테리어>수납가구>잡지꽂이'</li><li>'아크릴 양면 받침대 중 가구/인테리어>수납가구>잡지꽂이'</li><li>'인테리어용책 소품 잡지책 매거진 카페 쇼품 사무실 DP 가구/인테리어>수납가구>잡지꽂이'</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_fi5")
	# Run inference
	preds = model("아카시아 대용량 2단 선반 행거 400호 가구/인테리어>수납가구>행거")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 3 \| 9.0095 \| 20 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0.0 \| 69 \|
	\| 1.0 \| 70 \|
	\| 2.0 \| 70 \|
	\| 3.0 \| 70 \|
	\| 4.0 \| 70 \|
	\| 5.0 \| 70 \|
	\| 6.0 \| 70 \|
	\| 7.0 \| 70 \|
	\| 8.0 \| 70 \|
	\| 9.0 \| 69 \|
	\| 10.0 \| 70 \|
	\| 11.0 \| 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0061 \| 1 \| 0.4852 \| - \|
	\| 0.3049 \| 50 \| 0.4994 \| - \|
	\| 0.6098 \| 100 \| 0.4134 \| - \|
	\| 0.9146 \| 150 \| 0.1731 \| - \|
	\| 1.2195 \| 200 \| 0.0287 \| - \|
	\| 1.5244 \| 250 \| 0.0058 \| - \|
	\| 1.8293 \| 300 \| 0.0003 \| - \|
	\| 2.1341 \| 350 \| 0.0001 \| - \|
	\| 2.4390 \| 400 \| 0.0001 \| - \|
	\| 2.7439 \| 450 \| 0.0001 \| - \|
	\| 3.0488 \| 500 \| 0.0 \| - \|
	\| 3.3537 \| 550 \| 0.0 \| - \|
	\| 3.6585 \| 600 \| 0.0 \| - \|
	\| 3.9634 \| 650 \| 0.0 \| - \|
	\| 4.2683 \| 700 \| 0.0 \| - \|
	\| 4.5732 \| 750 \| 0.0 \| - \|
	\| 4.8780 \| 800 \| 0.0 \| - \|
	\| 5.1829 \| 850 \| 0.0 \| - \|
	\| 5.4878 \| 900 \| 0.0 \| - \|
	\| 5.7927 \| 950 \| 0.0 \| - \|
	\| 6.0976 \| 1000 \| 0.0 \| - \|
	\| 6.4024 \| 1050 \| 0.0 \| - \|
	\| 6.7073 \| 1100 \| 0.0 \| - \|
	\| 7.0122 \| 1150 \| 0.0002 \| - \|
	\| 7.3171 \| 1200 \| 0.0001 \| - \|
	\| 7.6220 \| 1250 \| 0.0 \| - \|
	\| 7.9268 \| 1300 \| 0.0 \| - \|
	\| 8.2317 \| 1350 \| 0.0 \| - \|
	\| 8.5366 \| 1400 \| 0.0 \| - \|
	\| 8.8415 \| 1450 \| 0.0 \| - \|
	\| 9.1463 \| 1500 \| 0.0 \| - \|
	\| 9.4512 \| 1550 \| 0.0 \| - \|
	\| 9.7561 \| 1600 \| 0.0 \| - \|
	\| 10.0610 \| 1650 \| 0.0 \| - \|
	\| 10.3659 \| 1700 \| 0.0 \| - \|
	\| 10.6707 \| 1750 \| 0.0 \| - \|
	\| 10.9756 \| 1800 \| 0.0 \| - \|
	\| 11.2805 \| 1850 \| 0.0 \| - \|
	\| 11.5854 \| 1900 \| 0.0 \| - \|
	\| 11.8902 \| 1950 \| 0.0 \| - \|
	\| 12.1951 \| 2000 \| 0.0 \| - \|
	\| 12.5 \| 2050 \| 0.0 \| - \|
	\| 12.8049 \| 2100 \| 0.0 \| - \|
	\| 13.1098 \| 2150 \| 0.0 \| - \|
	\| 13.4146 \| 2200 \| 0.0 \| - \|
	\| 13.7195 \| 2250 \| 0.0 \| - \|
	\| 14.0244 \| 2300 \| 0.0 \| - \|
	\| 14.3293 \| 2350 \| 0.0 \| - \|
	\| 14.6341 \| 2400 \| 0.0 \| - \|
	\| 14.9390 \| 2450 \| 0.0 \| - \|
	\| 15.2439 \| 2500 \| 0.0 \| - \|
	\| 15.5488 \| 2550 \| 0.0 \| - \|
	\| 15.8537 \| 2600 \| 0.0 \| - \|
	\| 16.1585 \| 2650 \| 0.0 \| - \|
	\| 16.4634 \| 2700 \| 0.0 \| - \|
	\| 16.7683 \| 2750 \| 0.0 \| - \|
	\| 17.0732 \| 2800 \| 0.0 \| - \|
	\| 17.3780 \| 2850 \| 0.0 \| - \|
	\| 17.6829 \| 2900 \| 0.0 \| - \|
	\| 17.9878 \| 2950 \| 0.0 \| - \|
	\| 18.2927 \| 3000 \| 0.0 \| - \|
	\| 18.5976 \| 3050 \| 0.0 \| - \|
	\| 18.9024 \| 3100 \| 0.0 \| - \|
	\| 19.2073 \| 3150 \| 0.0 \| - \|
	\| 19.5122 \| 3200 \| 0.0 \| - \|
	\| 19.8171 \| 3250 \| 0.0 \| - \|
	\| 20.1220 \| 3300 \| 0.0 \| - \|
	\| 20.4268 \| 3350 \| 0.0 \| - \|
	\| 20.7317 \| 3400 \| 0.0 \| - \|
	\| 21.0366 \| 3450 \| 0.0 \| - \|
	\| 21.3415 \| 3500 \| 0.0 \| - \|
	\| 21.6463 \| 3550 \| 0.0 \| - \|
	\| 21.9512 \| 3600 \| 0.0 \| - \|
	\| 22.2561 \| 3650 \| 0.0 \| - \|
	\| 22.5610 \| 3700 \| 0.0 \| - \|
	\| 22.8659 \| 3750 \| 0.0 \| - \|
	\| 23.1707 \| 3800 \| 0.0 \| - \|
	\| 23.4756 \| 3850 \| 0.0 \| - \|
	\| 23.7805 \| 3900 \| 0.0 \| - \|
	\| 24.0854 \| 3950 \| 0.0 \| - \|
	\| 24.3902 \| 4000 \| 0.0 \| - \|
	\| 24.6951 \| 4050 \| 0.0 \| - \|
	\| 25.0 \| 4100 \| 0.0 \| - \|
	\| 25.3049 \| 4150 \| 0.0 \| - \|
	\| 25.6098 \| 4200 \| 0.0 \| - \|
	\| 25.9146 \| 4250 \| 0.0 \| - \|
	\| 26.2195 \| 4300 \| 0.0 \| - \|
	\| 26.5244 \| 4350 \| 0.0 \| - \|
	\| 26.8293 \| 4400 \| 0.0 \| - \|
	\| 27.1341 \| 4450 \| 0.0 \| - \|
	\| 27.4390 \| 4500 \| 0.0 \| - \|
	\| 27.7439 \| 4550 \| 0.0 \| - \|
	\| 28.0488 \| 4600 \| 0.0 \| - \|
	\| 28.3537 \| 4650 \| 0.0 \| - \|
	\| 28.6585 \| 4700 \| 0.0 \| - \|
	\| 28.9634 \| 4750 \| 0.0 \| - \|
	\| 29.2683 \| 4800 \| 0.0 \| - \|
	\| 29.5732 \| 4850 \| 0.0 \| - \|
	\| 29.8780 \| 4900 \| 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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