master_cate_sl0 / README.md

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	---
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: 고급형 검도 손목보호대 검도보호대 일본산 스포츠/레저>검도>검도보호용품
	- text: 검좌대 검도 목검 거치대 사무라이검 받침대 플루트 진열대 검 스탠드 죽도 선반 스포츠/레저>검도>기타검도용품
	- text: 검도단 탁상 사무실용 대나무 디스플레이 Tier 478490 1 스포츠/레저>검도>검도보호용품
	- text: 스탠드 검도 타격대 타이어 죽도 훈련 연습 수련 도장 스포츠/레저>검도>타격대
	- text: 검거치대 좌대 거치대 진열대 검받침대 사극 무술 랙 소품 목도 스포츠/레저>검도>기타검도용품
	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 \|
	\|:------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 0.0 \| <ul><li>'검도 장갑 호완 보호대 호구 손 보호 장비 검도용품 스포츠/레저>검도>검도보호용품'</li><li>'검도호구 장비 보호대 호면 세트 갑옷 방어구 호완 머리 부품 입문용 손목 갑상 턱 초보자용 스포츠/레저>검도>검도보호용품'</li><li>'나인 더 가든 구사쿠라 체리 완제품 여아용 클로즈 토 가죽 미디엄 건축 t3l 스포츠/레저>검도>검도보호용품'</li></ul> \|
	\| 1.0 \| <ul><li>'호구가방 검도 캐리어 방어구 장비 대용량 배낭 백팩 스포츠/레저>검도>기타검도용품'</li><li>'목검 거치대 받침대 검도 검 진열대 죽도 보관함 홀더 스포츠/레저>검도>기타검도용품'</li><li>'검좌대 원목 랙 도검좌대 사극 무술 검도 목도 소품 장식용 스탠드 목검 선반 스포츠/레저>검도>기타검도용품'</li></ul> \|
	\| 2.0 \| <ul><li>'검도 백색 검도복 검도 도복 스포츠/레저 > 검도 > 도복'</li><li>'검도 도복 프리미엄 검도복 선 (SUN) 스포츠/레저 > 검도 > 도복'</li><li>'뉴페이스 도복 상하세트 C4B-2 스포츠/레저>검도>도복'</li></ul> \|
	\| 3.0 \| <ul><li>'성심 고도형 죽도 39호 스포츠/레저>검도>죽도'</li><li>'단심 시합용 죽도 39호 스포츠/레저>검도>죽도'</li><li>'충신 시합용 죽도 39호 스포츠/레저>검도>죽도'</li></ul> \|
	\| 4.0 \| <ul><li>'봉집 무술용품 목검가방 죽도가방 스포츠/레저 > 검도 > 죽도집/부속품'</li><li>'검가방/천검집(가검용) G-12D 스포츠/레저 > 검도 > 죽도집/부속품'</li><li>'검도 죽도 가방 목검 주머니 패브릭 목검집 스트랩 휴대용 스포츠/레저 > 검도 > 죽도집/부속품'</li></ul> \|
	\| 5.0 \| <ul><li>'목인장 원목 샌드백 영춘권 무술 복싱 목각 타격대 1 7m-옵션사진참조 스포츠/레저>검도>타격대'</li><li>'검도 연습 타격대 스탠드 죽도 훈련 수련 샌드백 더미 연습용 수련대 펜싱 타겟 기술 스포츠/레저>검도>타격대'</li><li>'검도 타격대 샌드백 찌르기 연습용 죽도 수련대 무술 스포츠/레저>검도>타격대'</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_sl0")
	# Run inference
	preds = model("고급형 검도 손목보호대 검도보호대 일본산 스포츠/레저>검도>검도보호용품")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 3 \| 9.5927 \| 19 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0.0 \| 70 \|
	\| 1.0 \| 70 \|
	\| 2.0 \| 12 \|
	\| 3.0 \| 15 \|
	\| 4.0 \| 11 \|
	\| 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0204 \| 1 \| 0.4824 \| - \|
	\| 1.0204 \| 50 \| 0.4133 \| - \|
	\| 2.0408 \| 100 \| 0.0315 \| - \|
	\| 3.0612 \| 150 \| 0.0021 \| - \|
	\| 4.0816 \| 200 \| 0.0001 \| - \|
	\| 5.1020 \| 250 \| 0.0 \| - \|
	\| 6.1224 \| 300 \| 0.0 \| - \|
	\| 7.1429 \| 350 \| 0.0 \| - \|
	\| 8.1633 \| 400 \| 0.0 \| - \|
	\| 9.1837 \| 450 \| 0.0 \| - \|
	\| 10.2041 \| 500 \| 0.0 \| - \|
	\| 11.2245 \| 550 \| 0.0 \| - \|
	\| 12.2449 \| 600 \| 0.0 \| - \|
	\| 13.2653 \| 650 \| 0.0 \| - \|
	\| 14.2857 \| 700 \| 0.0 \| - \|
	\| 15.3061 \| 750 \| 0.0 \| - \|
	\| 16.3265 \| 800 \| 0.0 \| - \|
	\| 17.3469 \| 850 \| 0.0 \| - \|
	\| 18.3673 \| 900 \| 0.0 \| - \|
	\| 19.3878 \| 950 \| 0.0 \| - \|
	\| 20.4082 \| 1000 \| 0.0 \| - \|
	\| 21.4286 \| 1050 \| 0.0 \| - \|
	\| 22.4490 \| 1100 \| 0.0 \| - \|
	\| 23.4694 \| 1150 \| 0.0 \| - \|
	\| 24.4898 \| 1200 \| 0.0 \| - \|
	\| 25.5102 \| 1250 \| 0.0 \| - \|
	\| 26.5306 \| 1300 \| 0.0 \| - \|
	\| 27.5510 \| 1350 \| 0.0 \| - \|
	\| 28.5714 \| 1400 \| 0.0 \| - \|
	\| 29.5918 \| 1450 \| 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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