master_cate_sl24 / README.md

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	---
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: 집업 여자 요가복 상의 필라테스복 긴팔 운동복 상의 스포츠/레저>요가/필라테스>요가복>상의
	- text: 요가 홈트 스트랩 벨트 필라테스 명상 레슨 플라잉 02 3세대 업그레이드 쿠션 스포츠/레저>요가/필라테스>기타요가용품
	- text: 스파인코렉터 홈트 가정용 운동 교정 허리 마사지기 스포츠/레저>요가/필라테스>필라테스
	- text: KKJN 남자반팔상의요가복 NT1105 스포츠/레저>요가/필라테스>요가복>상의
	- text: 필라테스 척추 스트레칭 스파인코렉터 허리 홈트 요가 스포츠/레저>요가/필라테스>필라테스
	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: 5 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 \|
	\|:------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 4.0 \| <ul><li>'기구 필라테스 체어 스프링 스트레칭 홈트 스텝체어 스포츠/레저>요가/필라테스>필라테스'</li><li>'체어 필라테스 기구 대형 장비 코어 운동 전신 홈짐 스포츠/레저>요가/필라테스>필라테스'</li><li>'집에서하는필라테스 레더바렐 레더배럴 원목 스트레칭 스포츠/레저>요가/필라테스>필라테스'</li></ul> \|
	\| 2.0 \| <ul><li>'밸런시스 NEW NR1 센터라인 요가매트 6.3mm 스포츠/레저>요가/필라테스>요가매트'</li><li>'아디다스 피트니스 매트 ADYG-10010 스포츠/레저>요가/필라테스>요가매트'</li><li>'듀잇 매트 + 스트랩 SET 스포츠/레저>요가/필라테스>요가매트'</li></ul> \|
	\| 3.0 \| <ul><li>'언더아머 커리 플리스 스웨트 팬츠 1374299001 스포츠/레저>요가/필라테스>요가복>하의'</li><li>'로라벨 무르 니트 필라테스복 여성 요가복 필라테스 요가 상의 옷 커버업 운동복 스포츠/레저>요가/필라테스>요가복>상의'</li><li>'데비웨어 여성 요가복 블랙 필라테스 서포트티 반팔티 DEVI-T0058 스포츠/레저>요가/필라테스>요가복>상의'</li></ul> \|
	\| 0.0 \| <ul><li>'그라데이션 플라잉 요가 해먹 고탄성 스포츠/레저>요가/필라테스>기타요가용품'</li><li>'자세 폴 요가 스트랩 - 스틱 벨트 스포츠/레저>요가/필라테스>기타요가용품'</li><li>'고정 공중 해먹 플라잉요가 요가 로프 천 스튜디오 스트레칭 필라테스 홈트 스포츠/레저>요가/필라테스>기타요가용품'</li></ul> \|
	\| 1.0 \| <ul><li>'요가 스트레칭 도구 요가링 필라테스링 허벅지 괄약근 다리 케겔운동 남성용 하체 힙업 항문 원형 팔 스포츠/레저>요가/필라테스>요가링/필라테스링'</li><li>'홈트 종아리운동 근육풀기용 혈액순환 하드 요가링 마사지 어깨결림 필라테스 스트레칭 스포츠/레저>요가/필라테스>요가링/필라테스링'</li><li>'홈트 운동 스트레칭 헬스 필라테스링 스포츠/레저>요가/필라테스>요가링/필라테스링'</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_sl24")
	# Run inference
	preds = model("KKJN 남자반팔상의요가복 NT1105 스포츠/레저>요가/필라테스>요가복>상의")
	```

	<!--
	### Downstream Use

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	-->

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	### Out-of-Scope Use

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	## Bias, Risks and Limitations

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 3 \| 8.2029 \| 17 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0.0 \| 70 \|
	\| 1.0 \| 70 \|
	\| 2.0 \| 70 \|
	\| 3.0 \| 70 \|
	\| 4.0 \| 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0145 \| 1 \| 0.4827 \| - \|
	\| 0.7246 \| 50 \| 0.4432 \| - \|
	\| 1.4493 \| 100 \| 0.0881 \| - \|
	\| 2.1739 \| 150 \| 0.0004 \| - \|
	\| 2.8986 \| 200 \| 0.0 \| - \|
	\| 3.6232 \| 250 \| 0.0 \| - \|
	\| 4.3478 \| 300 \| 0.0 \| - \|
	\| 5.0725 \| 350 \| 0.0 \| - \|
	\| 5.7971 \| 400 \| 0.0 \| - \|
	\| 6.5217 \| 450 \| 0.0 \| - \|
	\| 7.2464 \| 500 \| 0.0 \| - \|
	\| 7.9710 \| 550 \| 0.0 \| - \|
	\| 8.6957 \| 600 \| 0.0 \| - \|
	\| 9.4203 \| 650 \| 0.0 \| - \|
	\| 10.1449 \| 700 \| 0.0 \| - \|
	\| 10.8696 \| 750 \| 0.0 \| - \|
	\| 11.5942 \| 800 \| 0.0 \| - \|
	\| 12.3188 \| 850 \| 0.0 \| - \|
	\| 13.0435 \| 900 \| 0.0 \| - \|
	\| 13.7681 \| 950 \| 0.0 \| - \|
	\| 14.4928 \| 1000 \| 0.0 \| - \|
	\| 15.2174 \| 1050 \| 0.0 \| - \|
	\| 15.9420 \| 1100 \| 0.0 \| - \|
	\| 16.6667 \| 1150 \| 0.0 \| - \|
	\| 17.3913 \| 1200 \| 0.0 \| - \|
	\| 18.1159 \| 1250 \| 0.0 \| - \|
	\| 18.8406 \| 1300 \| 0.0 \| - \|
	\| 19.5652 \| 1350 \| 0.0 \| - \|
	\| 20.2899 \| 1400 \| 0.0 \| - \|
	\| 21.0145 \| 1450 \| 0.0 \| - \|
	\| 21.7391 \| 1500 \| 0.0 \| - \|
	\| 22.4638 \| 1550 \| 0.0 \| - \|
	\| 23.1884 \| 1600 \| 0.0 \| - \|
	\| 23.9130 \| 1650 \| 0.0 \| - \|
	\| 24.6377 \| 1700 \| 0.0 \| - \|
	\| 25.3623 \| 1750 \| 0.0 \| - \|
	\| 26.0870 \| 1800 \| 0.0 \| - \|
	\| 26.8116 \| 1850 \| 0.0 \| - \|
	\| 27.5362 \| 1900 \| 0.0 \| - \|
	\| 28.2609 \| 1950 \| 0.0 \| - \|
	\| 28.9855 \| 2000 \| 0.0 \| - \|
	\| 29.7101 \| 2050 \| 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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