master_cate_sl23 / README.md

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	---
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: 오픈페이스 스쿠터 오토바이 라이트 사계절 LED 남남녀공용 바이크 여성용 헬멧 스포츠/레저>오토바이/스쿠터>오토바이용품>헬멧
	- text: 코미네 오토바이 핀 잠금 장치 도난 방지 디스크락 열쇠 스포츠/레저>오토바이/스쿠터>오토바이부품>잠금장치
	- text: 방수 스즈끼 오토바이 방한복 배달 스즈키 우주복 겨울 오토바이 방한용품 스포츠/레저>오토바이/스쿠터>오토바이의류/잡화>상하세트
	- text: 오토바이 스쿠터 리어백 트렁크 소형 탑박스 두꺼운 범용 스포츠/레저>오토바이/스쿠터>오토바이용품>기타오토바이용품
	- text: 하템몰 오토바이 헬멧 블루투스 채터박스 BiT-3S 인터콤 스포츠/레저>오토바이/스쿠터>오토바이용품>기타오토바이용품
	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>'KR모터스 그란투스 125 스쿠터 스포츠/레저>오토바이/스쿠터>스쿠터>일반스쿠터'</li><li>'니키125 걸프 에디션 스쿠터 2024년 스포츠/레저>오토바이/스쿠터>스쿠터>일반스쿠터'</li><li>'AU테크 에코로 포니 전기스쿠터 12Ah 스포츠/레저>오토바이/스쿠터>스쿠터>전동스쿠터'</li></ul> \|
	\| 3.0 \| <ul><li>'오토바이 여름헬멧 오픈페이스 반모 경량 클래식 헬멧 빈티지 스틸 레트로 스포츠/레저>오토바이/스쿠터>오토바이용품>헬멧'</li><li>'레트로 바이크 헬멧 할리데이비슨 오토바이 감성 보 -레트로 미러 스포츠/레저>오토바이/스쿠터>오토바이용품>헬멧'</li><li>'남녀공용 편광 변색 선글라스 꽃가루 알레르기 방지 윈드 고글 UV400 보호 TR90 스포츠/레저>오토바이/스쿠터>오토바이용품>기타오토바이용품'</li></ul> \|
	\| 4.0 \| <ul><li>'코미네 3핑거 여름 반장갑 메쉬 글러브 BLACK-RED GK-260 스포츠/레저>오토바이/스쿠터>오토바이의류/잡화>바이크장갑'</li><li>'맥슬러 케블라 라이딩청바지 오토바이바지 M-2073 스포츠/레저>오토바이/스쿠터>오토바이의류/잡화>하의'</li><li>'오토바이 방한복 배달 남녀공용 일체형 방수 바이크 라이딩 배달우주복 스포츠/레저>오토바이/스쿠터>오토바이의류/잡화>상하세트'</li></ul> \|
	\| 2.0 \| <ul><li>'피렐리 엔젤 스쿠터 더뉴 PCX 뒤 타이어 120 스포츠/레저>오토바이/스쿠터>오토바이부품>기타오토바이부품'</li><li>'투스크 메가비트 레이디얼 타이어 28x1015 혼다 탈론 폭스 라이브 밸브용 1000X 스포츠/레저>오토바이/스쿠터>오토바이부품>기타오토바이부품'</li><li>'혼다 PCX125 브레이크 캘리퍼 핀 45215-KPH-951 스포츠/레저>오토바이/스쿠터>오토바이부품>기타오토바이부품'</li></ul> \|
	\| 1.0 \| <ul><li>'CFORCE 450 4륜 오토바이 스포츠/레저>오토바이/스쿠터>오토바이'</li><li>'ATV-A형 사륜오토바이 스포츠/레저>오토바이/스쿠터>오토바이'</li><li>'BMW F850GS 오토바이 스포츠/레저>오토바이/스쿠터>오토바이'</li></ul> \|
	\| 5.0 \| <ul><li>'자이로콥 G에스 전동투휠 4.4Ah 스포츠/레저>오토바이/스쿠터>전동휠'</li><li>'킹송 외발휠 16S 840Wh 스포츠/레저>오토바이/스쿠터>전동휠'</li><li>'나인봇 엘리트 플러스 전동휠 10Ah 스포츠/레저>오토바이/스쿠터>전동휠'</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_sl23")
	# Run inference
	preds = model("코미네 오토바이 핀 잠금 장치 도난 방지 디스크락 열쇠 스포츠/레저>오토바이/스쿠터>오토바이부품>잠금장치")
	```

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	### Downstream Use

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	### Out-of-Scope Use

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 3 \| 8.3719 \| 19 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0.0 \| 70 \|
	\| 1.0 \| 70 \|
	\| 2.0 \| 70 \|
	\| 3.0 \| 70 \|
	\| 4.0 \| 70 \|
	\| 5.0 \| 13 \|

	### 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0141 \| 1 \| 0.4943 \| - \|
	\| 0.7042 \| 50 \| 0.4622 \| - \|
	\| 1.4085 \| 100 \| 0.1572 \| - \|
	\| 2.1127 \| 150 \| 0.0427 \| - \|
	\| 2.8169 \| 200 \| 0.0006 \| - \|
	\| 3.5211 \| 250 \| 0.0 \| - \|
	\| 4.2254 \| 300 \| 0.0 \| - \|
	\| 4.9296 \| 350 \| 0.0 \| - \|
	\| 5.6338 \| 400 \| 0.0 \| - \|
	\| 6.3380 \| 450 \| 0.0 \| - \|
	\| 7.0423 \| 500 \| 0.0 \| - \|
	\| 7.7465 \| 550 \| 0.0 \| - \|
	\| 8.4507 \| 600 \| 0.0 \| - \|
	\| 9.1549 \| 650 \| 0.0 \| - \|
	\| 9.8592 \| 700 \| 0.0 \| - \|
	\| 10.5634 \| 750 \| 0.0 \| - \|
	\| 11.2676 \| 800 \| 0.0 \| - \|
	\| 11.9718 \| 850 \| 0.0 \| - \|
	\| 12.6761 \| 900 \| 0.0001 \| - \|
	\| 13.3803 \| 950 \| 0.0 \| - \|
	\| 14.0845 \| 1000 \| 0.0 \| - \|
	\| 14.7887 \| 1050 \| 0.0 \| - \|
	\| 15.4930 \| 1100 \| 0.0 \| - \|
	\| 16.1972 \| 1150 \| 0.0 \| - \|
	\| 16.9014 \| 1200 \| 0.0 \| - \|
	\| 17.6056 \| 1250 \| 0.0 \| - \|
	\| 18.3099 \| 1300 \| 0.0 \| - \|
	\| 19.0141 \| 1350 \| 0.0 \| - \|
	\| 19.7183 \| 1400 \| 0.0 \| - \|
	\| 20.4225 \| 1450 \| 0.0 \| - \|
	\| 21.1268 \| 1500 \| 0.0 \| - \|
	\| 21.8310 \| 1550 \| 0.0 \| - \|
	\| 22.5352 \| 1600 \| 0.0 \| - \|
	\| 23.2394 \| 1650 \| 0.0 \| - \|
	\| 23.9437 \| 1700 \| 0.0 \| - \|
	\| 24.6479 \| 1750 \| 0.0 \| - \|
	\| 25.3521 \| 1800 \| 0.0 \| - \|
	\| 26.0563 \| 1850 \| 0.0 \| - \|
	\| 26.7606 \| 1900 \| 0.0 \| - \|
	\| 27.4648 \| 1950 \| 0.0 \| - \|
	\| 28.1690 \| 2000 \| 0.0 \| - \|
	\| 28.8732 \| 2050 \| 0.0 \| - \|
	\| 29.5775 \| 2100 \| 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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