master_cate_top_bt13_9_test_flat / README.md

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	---
	base_model: mini1013/master_domain
	library_name: setfit
	metrics:
	- accuracy
	pipeline_tag: text-classification
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: 헤어샵 전용 바이오메드 엘피피 트리트먼트 LPP 실크 트리트먼트1000ml 사은품 증정 (#M)쿠팡 홈>뷰티>헤어>트리트먼트/팩>일반
	트리트먼트 Coupang > 뷰티 > 헤어 > 트리트먼트/팩 > 일반 트리트먼트
	- text: 미쟝센 퍼펙트 세럼 트리트먼트 330ml × 1개 (#M)쿠팡 홈>뷰티>헤어>트리트먼트/팩/앰플>일반 트리트먼트 Coupang > 뷰티
	> 헤어 > 트리트먼트/팩/앰플 > 일반 트리트먼트
	- text: 한소희Pick 로레알파리 토탈리페어5 트리트먼트 헤어팩 400ml 50ml 헤어팩280ml LotteOn > 뷰티 > 헤어/바디 >
	헤어케어 > 트리트먼트/헤어팩 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 트리트먼트/헤어팩
	- text: 밀크바오밥 오리지널 샴푸 화이트솝 1L(옵션선택1) 11 트리트먼트 화이트솝 1000ml (#M)헤어케어>샴푸>샴푸바 AD > traverse
	> 11st > 뷰티 > 헤어케어 > 샴푸 > 샴푸바
	- text: 로레알 토탈리페어5 헤어팩 280ml + 170ml (#M)쿠팡 홈>생활용품>헤어/바디/세안>트리트먼트/팩/앰플>헤어팩/헤어마스크
	Coupang > 뷰티 > 헤어 > 트리트먼트/팩/앰플 > 헤어팩/헤어마스크
	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: accuracy
	value: 0.8786919831223629
	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: 2 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 \|
	\|:------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 1 \| <ul><li>'[웰라] 염색모전용 SP 컬러 세이브 마스크 400ml (#M)화장품/미용>헤어케어>헤어팩 LO > window_fashion_town > Naverstore > FashionTown > 뷰티 > CATEGORY > 헤어케어 > 트리트먼트/팩 > 헤어팩'</li><li>'아모스 01 퓨어스마트 샴푸 팩 비듬케어 사춘기샴푸 퓨어 스마트 팩 300ml-비듬두피팩 (#M)홈>화장품/미용>헤어케어>샴푸 Naverstore > 화장품/미용 > 헤어케어 > 샴푸'</li><li>'미쟝센 데미지 케어 로즈프로틴 헤어팩 150ml × 1개 (#M)쿠팡 홈>생활용품>헤어/바디/세안>트리트먼트/팩/앰플>헤어팩/헤어마스크 Coupang > 뷰티 > 헤어 > 트리트먼트/팩/앰플 > 헤어팩/헤어마스크'</li></ul> \|
	\| 0 \| <ul><li>'스무드 인퓨전 너리싱 스타일링 크림 250ml LotteOn > 뷰티 > 명품화장품 > 헤어케어 LotteOn > 뷰티 > 헤어케어 > 헤어에센스'</li><li>'체리블라썸/아르간오일 트리트먼트 280ml x2개 02)모로코아르간 트리트먼트 2개 LotteOn > 뷰티 > 헤어케어 > 트리트먼트 LotteOn > 뷰티 > 헤어케어 > 트리트먼트'</li><li>'[LG생활건강] 비욘드 프로페셔널 디펜스 트리트먼트 500ml LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 린스 LotteOn > 뷰티 > 헤어/바디 > 헤어케어 > 린스'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.8787 \|

	## 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_top_bt13_9_test_flat")
	# Run inference
	preds = model("미쟝센 퍼펙트 세럼 트리트먼트 330ml × 1개 (#M)쿠팡 홈>뷰티>헤어>트리트먼트/팩/앰플>일반 트리트먼트 Coupang > 뷰티 > 헤어 > 트리트먼트/팩/앰플 > 일반 트리트먼트")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 11 \| 21.07 \| 49 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0 \| 50 \|
	\| 1 \| 50 \|

	### Training Hyperparameters
	- batch_size: (64, 64)
	- num_epochs: (30, 30)
	- max_steps: -1
	- sampling_strategy: oversampling
	- num_iterations: 100
	- 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0064 \| 1 \| 0.4262 \| - \|
	\| 0.3185 \| 50 \| 0.4176 \| - \|
	\| 0.6369 \| 100 \| 0.314 \| - \|
	\| 0.9554 \| 150 \| 0.0953 \| - \|
	\| 1.2739 \| 200 \| 0.0302 \| - \|
	\| 1.5924 \| 250 \| 0.0123 \| - \|
	\| 1.9108 \| 300 \| 0.0005 \| - \|
	\| 2.2293 \| 350 \| 0.0002 \| - \|
	\| 2.5478 \| 400 \| 0.0001 \| - \|
	\| 2.8662 \| 450 \| 0.0001 \| - \|
	\| 3.1847 \| 500 \| 0.0001 \| - \|
	\| 3.5032 \| 550 \| 0.0 \| - \|
	\| 3.8217 \| 600 \| 0.0001 \| - \|
	\| 4.1401 \| 650 \| 0.0 \| - \|
	\| 4.4586 \| 700 \| 0.0 \| - \|
	\| 4.7771 \| 750 \| 0.0 \| - \|
	\| 5.0955 \| 800 \| 0.0001 \| - \|
	\| 5.4140 \| 850 \| 0.0001 \| - \|
	\| 5.7325 \| 900 \| 0.0 \| - \|
	\| 6.0510 \| 950 \| 0.0 \| - \|
	\| 6.3694 \| 1000 \| 0.0 \| - \|
	\| 6.6879 \| 1050 \| 0.0 \| - \|
	\| 7.0064 \| 1100 \| 0.0 \| - \|
	\| 7.3248 \| 1150 \| 0.0 \| - \|
	\| 7.6433 \| 1200 \| 0.0 \| - \|
	\| 7.9618 \| 1250 \| 0.0 \| - \|
	\| 8.2803 \| 1300 \| 0.0 \| - \|
	\| 8.5987 \| 1350 \| 0.0 \| - \|
	\| 8.9172 \| 1400 \| 0.0 \| - \|
	\| 9.2357 \| 1450 \| 0.0 \| - \|
	\| 9.5541 \| 1500 \| 0.0 \| - \|
	\| 9.8726 \| 1550 \| 0.0 \| - \|
	\| 10.1911 \| 1600 \| 0.0 \| - \|
	\| 10.5096 \| 1650 \| 0.0 \| - \|
	\| 10.8280 \| 1700 \| 0.0 \| - \|
	\| 11.1465 \| 1750 \| 0.0 \| - \|
	\| 11.4650 \| 1800 \| 0.0 \| - \|
	\| 11.7834 \| 1850 \| 0.0 \| - \|
	\| 12.1019 \| 1900 \| 0.0 \| - \|
	\| 12.4204 \| 1950 \| 0.0 \| - \|
	\| 12.7389 \| 2000 \| 0.0 \| - \|
	\| 13.0573 \| 2050 \| 0.0 \| - \|
	\| 13.3758 \| 2100 \| 0.0 \| - \|
	\| 13.6943 \| 2150 \| 0.0 \| - \|
	\| 14.0127 \| 2200 \| 0.0 \| - \|
	\| 14.3312 \| 2250 \| 0.0 \| - \|
	\| 14.6497 \| 2300 \| 0.0 \| - \|
	\| 14.9682 \| 2350 \| 0.0 \| - \|
	\| 15.2866 \| 2400 \| 0.0 \| - \|
	\| 15.6051 \| 2450 \| 0.0 \| - \|
	\| 15.9236 \| 2500 \| 0.0 \| - \|
	\| 16.2420 \| 2550 \| 0.0 \| - \|
	\| 16.5605 \| 2600 \| 0.0 \| - \|
	\| 16.8790 \| 2650 \| 0.0 \| - \|
	\| 17.1975 \| 2700 \| 0.0001 \| - \|
	\| 17.5159 \| 2750 \| 0.0001 \| - \|
	\| 17.8344 \| 2800 \| 0.0003 \| - \|
	\| 18.1529 \| 2850 \| 0.0 \| - \|
	\| 18.4713 \| 2900 \| 0.0 \| - \|
	\| 18.7898 \| 2950 \| 0.0 \| - \|
	\| 19.1083 \| 3000 \| 0.0 \| - \|
	\| 19.4268 \| 3050 \| 0.0 \| - \|
	\| 19.7452 \| 3100 \| 0.0001 \| - \|
	\| 20.0637 \| 3150 \| 0.0002 \| - \|
	\| 20.3822 \| 3200 \| 0.0 \| - \|
	\| 20.7006 \| 3250 \| 0.0 \| - \|
	\| 21.0191 \| 3300 \| 0.0 \| - \|
	\| 21.3376 \| 3350 \| 0.0 \| - \|
	\| 21.6561 \| 3400 \| 0.0 \| - \|
	\| 21.9745 \| 3450 \| 0.0 \| - \|
	\| 22.2930 \| 3500 \| 0.0 \| - \|
	\| 22.6115 \| 3550 \| 0.0 \| - \|
	\| 22.9299 \| 3600 \| 0.0 \| - \|
	\| 23.2484 \| 3650 \| 0.0 \| - \|
	\| 23.5669 \| 3700 \| 0.0 \| - \|
	\| 23.8854 \| 3750 \| 0.0 \| - \|
	\| 24.2038 \| 3800 \| 0.0 \| - \|
	\| 24.5223 \| 3850 \| 0.0 \| - \|
	\| 24.8408 \| 3900 \| 0.0 \| - \|
	\| 25.1592 \| 3950 \| 0.0 \| - \|
	\| 25.4777 \| 4000 \| 0.0 \| - \|
	\| 25.7962 \| 4050 \| 0.0 \| - \|
	\| 26.1146 \| 4100 \| 0.0 \| - \|
	\| 26.4331 \| 4150 \| 0.0 \| - \|
	\| 26.7516 \| 4200 \| 0.0 \| - \|
	\| 27.0701 \| 4250 \| 0.0 \| - \|
	\| 27.3885 \| 4300 \| 0.0 \| - \|
	\| 27.7070 \| 4350 \| 0.0 \| - \|
	\| 28.0255 \| 4400 \| 0.0 \| - \|
	\| 28.3439 \| 4450 \| 0.0 \| - \|
	\| 28.6624 \| 4500 \| 0.0 \| - \|
	\| 28.9809 \| 4550 \| 0.0 \| - \|
	\| 29.2994 \| 4600 \| 0.0 \| - \|
	\| 29.6178 \| 4650 \| 0.0 \| - \|
	\| 29.9363 \| 4700 \| 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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