master_cate_lh11 / README.md

Push model using huggingface_hub.

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	---
	base_model: mini1013/master_domain
	library_name: setfit
	metrics:
	- metric
	pipeline_tag: text-classification
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: 칸토스 남여 기능성 다이어트 지압슬리퍼 5. 신여성자갈_240 온누리산업
	- text: 국산 헤라칸 케나프 케냐프 발 발바닥 지압 건강 슬리퍼 실내화 연핑크(M) 예일마켓
	- text: 풀리오 종아리마사지기 V3 디와이shop
	- text: 풋 브러쉬 발각질 제거 마사지 큐오랩
	- text: 마사지 실내 발지압매트 돌지압판 50x200CM보보보생꽃롱 도치글로벌
	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: metric
	value: 0.9710123383380407
	name: Metric
	---

	# 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: 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 \|
	\|:------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 6.0 \| <ul><li>'자갈 매트 지압판 조약돌 발판 지압길 지압 발매트 40X60CM 컬러풀 에이알'</li><li>'뽀송뽀송 메모리폼 발닦개매트 욕실 주방 발매트 러그 대형 면 화장실 라지 50X80_레드 시그나몰'</li><li>'굳은 살 걱정없는 특허기술 헬스핀 발 지압매트 지압판 부모님 선물 효도 헬스핀 운동 기획상품_핑크 붐코리아'</li></ul> \|
	\| 1.0 \| <ul><li>'김수자 엔젤 종아리 발마사지기 다리안마기 GKM-1004 닥터PLUS'</li><li>'듀플렉스 쎄라웨어 온열찜질 발마사지기 DP-FM700 신인선'</li><li>'7만원대 추가할인 여름휴가필수 [ ]업그레이드 3세대 스마트센서 종아리부터 허벅지까지 붓기 싹!! 무선다리마사지기 SR-S1+슬리밍삭스(옐로우) 수련닷컴'</li></ul> \|
	\| 5.0 \| <ul><li>'발각질양말 실리콘 패드 발보습 양말 발각질 케어 화이트 석진케이 주식회사'</li><li>'[BZJKWP4I_49]irbrush 뒤꿈치 패드 풋케어 발각질 3.블랙(5mm)FREE 롯데아이몰'</li><li>'일상공감 보드랍족 발보호대 1+1 뒤꿈치 발각질 보습풋패드 양말 발보호대 1+1_스킨 L 1쌍+화이트 M 1쌍 주식회사 이공구오'</li></ul> \|
	\| 4.0 \| <ul><li>'쾌발Q 60매 발냄새제거제/유해세균억제/무좀/발관리 해피MART'</li><li>'편백나무 슈즈프레쉬 신발장 옷장 탈취 발냄새제거 제습 방향효과 크레비스'</li><li>'[공식수입] 발냄새제거제 그랜즈레미디 페퍼민트향 cscosmetics'</li></ul> \|
	\| 0.0 \| <ul><li>'평발 아치 슬리퍼 발바닥 통증 완화 아치슬리퍼 사파이어 블루_290 탱큐'</li><li>'통굽 지압슬리퍼 실내화 층간소음방지 미끄럼방지 욕실화 도톨 지압 옐로우 39-40 9025 파인메탈릭'</li><li>'[낫소]낫소 지압2 슬리퍼 아이보리/230 패션플러스'</li></ul> \|
	\| 3.0 \| <ul><li>'바렌 매직 스텐 양면 프로 발각질제거기 포유어뷰티'</li><li>'바렌 패디퍼펙트 전동 발각질 제거기 퍼플에디션 발 뒤꿈치 발바닥 굳은살 제거 패디플래닝 1세트(사은품 증정)_리뷰약속 x (주)마르스랩스'</li><li>'오제끄 실크풋 버퍼 단품 인앤인마켓'</li></ul> \|
	\| 7.0 \| <ul><li>'[리퍼브]굿프렌드 밸런스휴 건식좌훈족욕기 GOOD-F5 리퍼브 건식좌훈족욕기 GOOD-F5 주식회사 굿테크'</li><li>'GSN-1610 편백나무 원적외선 건식 좌훈기+족욕기 겸용 MinSellAmount 온유어핏'</li><li>'B 굿프렌드 캐나다산 소나무 원목 스마트 건식족욕기 GOOD-F4 휴게실 가정용 마켓뷰'</li></ul> \|
	\| 2.0 \| <ul><li>'염색도구세트 셀프 키트 볼 브러쉬 가정용 헤어 브러시 빗 머리 모발 간편 07.Aeib 염색빗세트3P_본상품선택 주식회사유마켓'</li><li>'OC1242 손가락 발가락 관절보호 보습 실리콘 골무18종 통기화이트S(12425) 테익디스(TAKE THIS)'</li><li>'OC1242 손가락 발가락 보호 보습 실리콘 골무18종 실리콘골무 구멍뚫린골무 발가락 보호 골무스킨톤L(11033) 제이한 주식회사'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Metric \|
	\|:--------\|:-------\|
	\| all \| 0.9710 \|

	## 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_lh11")
	# Run inference
	preds = model("풋 브러쉬 발각질 제거 마사지 큐오랩")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 3 \| 9.9325 \| 21 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0.0 \| 50 \|
	\| 1.0 \| 50 \|
	\| 2.0 \| 50 \|
	\| 3.0 \| 50 \|
	\| 4.0 \| 50 \|
	\| 5.0 \| 50 \|
	\| 6.0 \| 50 \|
	\| 7.0 \| 50 \|

	### Training Hyperparameters
	- batch_size: (512, 512)
	- num_epochs: (20, 20)
	- max_steps: -1
	- sampling_strategy: oversampling
	- num_iterations: 40
	- 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0159 \| 1 \| 0.4383 \| - \|
	\| 0.7937 \| 50 \| 0.2003 \| - \|
	\| 1.5873 \| 100 \| 0.0636 \| - \|
	\| 2.3810 \| 150 \| 0.0158 \| - \|
	\| 3.1746 \| 200 \| 0.0239 \| - \|
	\| 3.9683 \| 250 \| 0.0153 \| - \|
	\| 4.7619 \| 300 \| 0.0004 \| - \|
	\| 5.5556 \| 350 \| 0.0023 \| - \|
	\| 6.3492 \| 400 \| 0.0005 \| - \|
	\| 7.1429 \| 450 \| 0.0002 \| - \|
	\| 7.9365 \| 500 \| 0.0001 \| - \|
	\| 8.7302 \| 550 \| 0.0001 \| - \|
	\| 9.5238 \| 600 \| 0.0001 \| - \|
	\| 10.3175 \| 650 \| 0.0001 \| - \|
	\| 11.1111 \| 700 \| 0.0001 \| - \|
	\| 11.9048 \| 750 \| 0.0001 \| - \|
	\| 12.6984 \| 800 \| 0.0 \| - \|
	\| 13.4921 \| 850 \| 0.0001 \| - \|
	\| 14.2857 \| 900 \| 0.0001 \| - \|
	\| 15.0794 \| 950 \| 0.0001 \| - \|
	\| 15.8730 \| 1000 \| 0.0 \| - \|
	\| 16.6667 \| 1050 \| 0.0001 \| - \|
	\| 17.4603 \| 1100 \| 0.0 \| - \|
	\| 18.2540 \| 1150 \| 0.0001 \| - \|
	\| 19.0476 \| 1200 \| 0.0001 \| - \|
	\| 19.8413 \| 1250 \| 0.0 \| - \|

	### Framework Versions
	- Python: 3.10.12
	- SetFit: 1.1.0.dev0
	- Sentence Transformers: 3.1.1
	- Transformers: 4.46.1
	- 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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