master_cate_sl19 / README.md

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	---
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: 발열양말 방한 보온양말 등산 낚시 스키 스노우보드 스케이트 야외작업 스포츠/레저>스키/보드>스키/보드방한용품>양말
	- text: 무크 엠 무크 펠로 데크 다크네이비 517413203ZB 스포츠/레저>스키/보드>스노보드장비>데크
	- text: 스키복 성인 자켓 상의 여성용 JACKET 스키자켓 남성 스포츠/레저>스키/보드>스키복>상의
	- text: Toko Edge Tuner Pro 스노우보드 엣지 튜닝 컷팅 스포츠/레저>스키/보드>스키/보드용품>보수장비
	- text: 헬리아 주니어 고글 카이로스 무광퍼플블랙 보드고글 스포츠고글 스포츠/레저>스키/보드>스키/보드용품>고글
	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>'충전식 열선양말 발열 스키장 보드 스키 등산 스포츠/레저>스키/보드>스키/보드방한용품>양말'</li><li>'우주마켓 겨울 방한 마스크 보온 등산 골프 따뜻한 자전거 귀덮개 귀마개 마스크 스포츠/레저>스키/보드>스키/보드방한용품>귀마개'</li><li>'다이나핏 폴디드 스몰로고 비니 Dark 스포츠/레저>스키/보드>스키/보드방한용품>비니'</li></ul> \|
	\| 0.0 \| <ul><li>'방풍 방수 여성 스노우 보드 플레이 여자 복 어스투 점퍼 점프 슈트 수트 스키 가프 스포츠/레저>스키/보드>보드복>재킷'</li><li>'2023 여성용 원피스 스키 슈트 겨울 야외 스포츠 방풍 방수 보온 스노보드 점프슈트 스포츠/레저>스키/보드>보드복>상하세트'</li><li>'여성용 스노우보드 점프수트 여성 일체형 스키복 방 -남성용 민트 그린 수트 스포츠/레저>스키/보드>보드복>상하세트'</li></ul> \|
	\| 5.0 \| <ul><li>'2223 헤드 스키 PURE JOY 여성용 스포츠/레저>스키/보드>스키장비>플레이트'</li><li>'미니 스키 부츠 스케이트 썰매 스노우 숏부츠 스포츠/레저>스키/보드>스키장비>부츠'</li><li>'PHOENIX 피닉스 주니어 스키 팀복 2223 PHENIX KOREA JR TEAM RD 스포츠/레저>스키/보드>스키장비>플레이트'</li></ul> \|
	\| 4.0 \| <ul><li>'스키복 세트 여성 남성 방한 방풍 스포츠/레저>스키/보드>스키복>상하세트'</li><li>'스파이더 남성 보르미오 GTX 스키 팬츠 SPFWCISP401MBLK LE1216929158 스포츠/레저>스키/보드>스키복>하의'</li><li>'카르포스 스키바지 남자 겨울 2521013 스포츠/레저>스키/보드>스키복>하의'</li></ul> \|
	\| 3.0 \| <ul><li>'XCMAN 4겹콘 스터드 디아 7 87인치 알루미늄 스노우보드 스톰프 패드 9pcs 스포츠/레저>스키/보드>스키/보드용품>스티커용품'</li><li>'Thule RoundTrip 스키 스노보드 더플 백 90L 다크 슬레이트 142322 스포츠/레저>스키/보드>스키/보드용품>보드가방'</li><li>'ToeJamR 스노우보드 스톰프 패드 나비 스포츠/레저>스키/보드>스키/보드용품>스티커용품'</li></ul> \|
	\| 1.0 \| <ul><li>'스노우 스키 여성 부츠 보드 롱 털 따듯한 스노보드 스포츠/레저>스키/보드>스노보드장비>부츠'</li><li>'나이트로 팀 바인딩 2223 NITRO Team 스포츠/레저>스키/보드>스노보드장비>바인딩'</li><li>'헌터 WOMEN 인트레피드 리플렉티브 카모 숏 스노우부츠 - 패턴그레이 WFS1004PCTPTG 스포츠/레저>스키/보드>스노보드장비>부츠'</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_sl19")
	# Run inference
	preds = model("스키복 성인 자켓 상의 여성용 JACKET 스키자켓 남성 스포츠/레저>스키/보드>스키복>상의")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 4 \| 9.4619 \| 18 \|

	\| 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 \| 0.4926 \| - \|
	\| 0.6024 \| 50 \| 0.497 \| - \|
	\| 1.2048 \| 100 \| 0.5003 \| - \|
	\| 1.8072 \| 150 \| 0.1918 \| - \|
	\| 2.4096 \| 200 \| 0.0218 \| - \|
	\| 3.0120 \| 250 \| 0.0004 \| - \|
	\| 3.6145 \| 300 \| 0.0003 \| - \|
	\| 4.2169 \| 350 \| 0.0001 \| - \|
	\| 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 \| 0.0001 \| - \|
	\| 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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