Push model using huggingface_hub.

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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: 1분완성 네일팁 모음인조손톱 인조팁 붙이는네일팁 웨딩네 13)샤인네일팁-화이트 LotteOn > 뷰티 > 네일 > 네일스티커/네일팁
	LotteOn > 뷰티 > 네일 > 네일스티커/네일팁
	- text: 오피아이 인피니트샤인2 매니큐어 MI12 × 1개 (#M)쿠팡 홈>뷰티>네일>일반네일>컬러 매니큐어 Coupang > 뷰티 > 네일
	> 일반네일 > 컬러 매니큐어
	- text: 오피아이 젤 네일 컬러 GCV33 x 1개 (#M)쿠팡 홈>뷰티>네일>일반네일>컬러 매니큐어 Coupang > 뷰티 > 네일 > 일반네일
	> 컬러 매니큐어
	- text: 디올 베르니 212 튀튀 LotteOn > 뷰티 > 메이크업 > 메이크업세트 LotteOn > 뷰티 > 메이크업 > 메이크업세트
	- text: OPI 인피니트샤인 HRL31 LETS BE FRIENDS HRL31 - LETS BE FRIENDS! LotteOn > 뷰티 > 헤어/바디
	> 헤어스타일링 > 염색/매니큐어 LotteOn > 뷰티 > 헤어/바디 > 헤어스타일링 > 염색/매니큐어
	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: 0.5301810865191147
	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: 4 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 \|
	\|:------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 3 \| <ul><li>'네일팁 실크익스텐션 311160L1720771597 티타늄금 물방울 （풀값 ） LotteOn > 뷰티 > 네일케어 > 네일케어도구 > 손톱깎이 LotteOn > 뷰티 > 네일케어 > 네일케어도구 > 손톱깎이'</li><li>'엔비베베 어린이 화장품 선물세트 어린이 썬쿠션+키즈네일스티커+워시패드 1개 (#M)쿠팡 홈>뷰티>어린이화장품>세트/키트 Coupang > 뷰티 > 어린이화장품 > 세트/키트'</li><li>'래쉬톡 원터치 인조 속눈썹 섹시 걸 × 3개입 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품 > 속눈썹관리 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품 > 속눈썹관리'</li></ul> \|
	\| 0 \| <ul><li>'오피아이 넌아세톤 리무버 빨강 30ml × 5개 (#M)쿠팡 홈>뷰티>네일>일반네일>리무버 Coupang > 뷰티 > 네일 > 일반네일 > 리무버'</li><li>'[OPI][리무버] 넌아세톤리무버 30ml ssg > 뷰티 > 메이크업 > 네일 ssg > 뷰티 > 메이크업 > 네일'</li><li>'포먼트 젤네일 O.4 블러쉬 뷰티 × 1개 (#M)쿠팡 홈>뷰티>네일>젤네일>컬러 젤 Coupang > 뷰티 > 네일 > 젤네일 > 컬러 젤'</li></ul> \|
	\| 2 \| <ul><li>'오피아이 프로스파 오일투고 큐티클 오일2197877 1 7.5ml x 1개2197877 1 (#M)SSG.COM/메이크업/베이스메이크업/컨실러 ssg > 뷰티 > 메이크업 > 베이스메이크업 > 컨실러'</li><li>'구찌 뷰티 [구찌] 베르니 아 옹글 하이 샤인 네일 라커 712 멜린다 그린 × 선택완료 (#M)쿠팡 홈>뷰티>네일>일반네일>컬러 매니큐어 Coupang > 뷰티 > 네일 > 일반네일 > 컬러 매니큐어'</li><li>'OPI ProSpa 각질 제거 큐티클 크림, 27ml SSG.COM/메이크업/베이스메이크업/메이크업베이스;ssg > 뷰티 > 메이크업 > 베이스메이크업 > 메이크업베이스 ssg > 뷰티 > 메이크업 > 베이스메이크업 > 메이크업베이스'</li></ul> \|
	\| 1 \| <ul><li>'르 베르니 루쥬 느와르 DepartmentLotteOn > 뷰티 > 헤어/바디 > 핸드/풋케어 > 네일케어 DepartmentLotteOn > 뷰티 > 헤어/바디 > 핸드/풋케어 > 네일케어'</li><li>'베씨 베이스젤 + 탑젤 + 지브라파일 2p 세트 베이스젤, 탑젤, 지브라파일(100/150) × 1세트 LotteOn > 뷰티 > 네일 > 네일아트소품 LotteOn > 뷰티 > 네일 > 네일아트소품'</li><li>'OPI OPI Chrome Effects Nail Lacquer Top Coat CPT31 - 0.5 oz 상세내용참조 × 상세내용참조 (#M)쿠팡 홈>뷰티>메이크업>베이스 메이크업>베이스/프라이머 Coupang > 뷰티 > 메이크업 > 베이스 메이크업 > 베이스/프라이머'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.5302 \|

	## 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_bt1_test_flat_top_cate")
	# Run inference
	preds = model("디올 베르니 212 튀튀 LotteOn > 뷰티 > 메이크업 > 메이크업세트 LotteOn > 뷰티 > 메이크업 > 메이크업세트")
	```

	<!--
	### Downstream Use

	List how someone could finetune this model on their own dataset.
	-->

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	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
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	<!--
	## Bias, Risks and Limitations

	What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.
	-->

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	### Recommendations

	What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.
	-->

	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:--------\|:----\|
	\| Word count \| 13 \| 22.7236 \| 41 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0 \| 49 \|
	\| 1 \| 50 \|
	\| 2 \| 50 \|
	\| 3 \| 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0032 \| 1 \| 0.4603 \| - \|
	\| 0.1608 \| 50 \| 0.4502 \| - \|
	\| 0.3215 \| 100 \| 0.4315 \| - \|
	\| 0.4823 \| 150 \| 0.3996 \| - \|
	\| 0.6431 \| 200 \| 0.365 \| - \|
	\| 0.8039 \| 250 \| 0.2954 \| - \|
	\| 0.9646 \| 300 \| 0.2647 \| - \|
	\| 1.1254 \| 350 \| 0.2378 \| - \|
	\| 1.2862 \| 400 \| 0.2257 \| - \|
	\| 1.4469 \| 450 \| 0.2165 \| - \|
	\| 1.6077 \| 500 \| 0.213 \| - \|
	\| 1.7685 \| 550 \| 0.1999 \| - \|
	\| 1.9293 \| 600 \| 0.1838 \| - \|
	\| 2.0900 \| 650 \| 0.1614 \| - \|
	\| 2.2508 \| 700 \| 0.1164 \| - \|
	\| 2.4116 \| 750 \| 0.0553 \| - \|
	\| 2.5723 \| 800 \| 0.0366 \| - \|
	\| 2.7331 \| 850 \| 0.0279 \| - \|
	\| 2.8939 \| 900 \| 0.0219 \| - \|
	\| 3.0547 \| 950 \| 0.0166 \| - \|
	\| 3.2154 \| 1000 \| 0.0111 \| - \|
	\| 3.3762 \| 1050 \| 0.0067 \| - \|
	\| 3.5370 \| 1100 \| 0.0084 \| - \|
	\| 3.6977 \| 1150 \| 0.0066 \| - \|
	\| 3.8585 \| 1200 \| 0.0048 \| - \|
	\| 4.0193 \| 1250 \| 0.0028 \| - \|
	\| 4.1801 \| 1300 \| 0.0005 \| - \|
	\| 4.3408 \| 1350 \| 0.0003 \| - \|
	\| 4.5016 \| 1400 \| 0.0004 \| - \|
	\| 4.6624 \| 1450 \| 0.0001 \| - \|
	\| 4.8232 \| 1500 \| 0.0001 \| - \|
	\| 4.9839 \| 1550 \| 0.0001 \| - \|
	\| 5.1447 \| 1600 \| 0.0001 \| - \|
	\| 5.3055 \| 1650 \| 0.0001 \| - \|
	\| 5.4662 \| 1700 \| 0.0002 \| - \|
	\| 5.6270 \| 1750 \| 0.0 \| - \|
	\| 5.7878 \| 1800 \| 0.0 \| - \|
	\| 5.9486 \| 1850 \| 0.0 \| - \|
	\| 6.1093 \| 1900 \| 0.0001 \| - \|
	\| 6.2701 \| 1950 \| 0.0 \| - \|
	\| 6.4309 \| 2000 \| 0.0 \| - \|
	\| 6.5916 \| 2050 \| 0.0 \| - \|
	\| 6.7524 \| 2100 \| 0.0 \| - \|
	\| 6.9132 \| 2150 \| 0.0002 \| - \|
	\| 7.0740 \| 2200 \| 0.0002 \| - \|
	\| 7.2347 \| 2250 \| 0.0 \| - \|
	\| 7.3955 \| 2300 \| 0.0 \| - \|
	\| 7.5563 \| 2350 \| 0.0 \| - \|
	\| 7.7170 \| 2400 \| 0.0 \| - \|
	\| 7.8778 \| 2450 \| 0.0 \| - \|
	\| 8.0386 \| 2500 \| 0.0 \| - \|
	\| 8.1994 \| 2550 \| 0.0 \| - \|
	\| 8.3601 \| 2600 \| 0.0 \| - \|
	\| 8.5209 \| 2650 \| 0.0 \| - \|
	\| 8.6817 \| 2700 \| 0.0 \| - \|
	\| 8.8424 \| 2750 \| 0.0 \| - \|
	\| 9.0032 \| 2800 \| 0.0 \| - \|
	\| 9.1640 \| 2850 \| 0.0 \| - \|
	\| 9.3248 \| 2900 \| 0.0 \| - \|
	\| 9.4855 \| 2950 \| 0.0 \| - \|
	\| 9.6463 \| 3000 \| 0.0 \| - \|
	\| 9.8071 \| 3050 \| 0.0 \| - \|
	\| 9.9678 \| 3100 \| 0.0 \| - \|
	\| 10.1286 \| 3150 \| 0.0 \| - \|
	\| 10.2894 \| 3200 \| 0.0 \| - \|
	\| 10.4502 \| 3250 \| 0.0 \| - \|
	\| 10.6109 \| 3300 \| 0.0 \| - \|
	\| 10.7717 \| 3350 \| 0.0 \| - \|
	\| 10.9325 \| 3400 \| 0.0 \| - \|
	\| 11.0932 \| 3450 \| 0.0 \| - \|
	\| 11.2540 \| 3500 \| 0.0 \| - \|
	\| 11.4148 \| 3550 \| 0.0 \| - \|
	\| 11.5756 \| 3600 \| 0.0 \| - \|
	\| 11.7363 \| 3650 \| 0.0 \| - \|
	\| 11.8971 \| 3700 \| 0.0 \| - \|
	\| 12.0579 \| 3750 \| 0.0004 \| - \|
	\| 12.2186 \| 3800 \| 0.0 \| - \|
	\| 12.3794 \| 3850 \| 0.0001 \| - \|
	\| 12.5402 \| 3900 \| 0.0001 \| - \|
	\| 12.7010 \| 3950 \| 0.0 \| - \|
	\| 12.8617 \| 4000 \| 0.0001 \| - \|
	\| 13.0225 \| 4050 \| 0.0002 \| - \|
	\| 13.1833 \| 4100 \| 0.0009 \| - \|
	\| 13.3441 \| 4150 \| 0.0037 \| - \|
	\| 13.5048 \| 4200 \| 0.0025 \| - \|
	\| 13.6656 \| 4250 \| 0.0009 \| - \|
	\| 13.8264 \| 4300 \| 0.0002 \| - \|
	\| 13.9871 \| 4350 \| 0.0002 \| - \|
	\| 14.1479 \| 4400 \| 0.0 \| - \|
	\| 14.3087 \| 4450 \| 0.0002 \| - \|
	\| 14.4695 \| 4500 \| 0.0001 \| - \|
	\| 14.6302 \| 4550 \| 0.0004 \| - \|
	\| 14.7910 \| 4600 \| 0.0008 \| - \|
	\| 14.9518 \| 4650 \| 0.0 \| - \|
	\| 15.1125 \| 4700 \| 0.0 \| - \|
	\| 15.2733 \| 4750 \| 0.0001 \| - \|
	\| 15.4341 \| 4800 \| 0.0 \| - \|
	\| 15.5949 \| 4850 \| 0.0 \| - \|
	\| 15.7556 \| 4900 \| 0.0002 \| - \|
	\| 15.9164 \| 4950 \| 0.0 \| - \|
	\| 16.0772 \| 5000 \| 0.0 \| - \|
	\| 16.2379 \| 5050 \| 0.0001 \| - \|
	\| 16.3987 \| 5100 \| 0.0 \| - \|
	\| 16.5595 \| 5150 \| 0.0 \| - \|
	\| 16.7203 \| 5200 \| 0.0 \| - \|
	\| 16.8810 \| 5250 \| 0.0 \| - \|
	\| 17.0418 \| 5300 \| 0.0 \| - \|
	\| 17.2026 \| 5350 \| 0.0 \| - \|
	\| 17.3633 \| 5400 \| 0.0 \| - \|
	\| 17.5241 \| 5450 \| 0.0 \| - \|
	\| 17.6849 \| 5500 \| 0.0 \| - \|
	\| 17.8457 \| 5550 \| 0.0 \| - \|
	\| 18.0064 \| 5600 \| 0.0 \| - \|
	\| 18.1672 \| 5650 \| 0.0 \| - \|
	\| 18.3280 \| 5700 \| 0.0 \| - \|
	\| 18.4887 \| 5750 \| 0.0 \| - \|
	\| 18.6495 \| 5800 \| 0.0 \| - \|
	\| 18.8103 \| 5850 \| 0.0 \| - \|
	\| 18.9711 \| 5900 \| 0.0 \| - \|
	\| 19.1318 \| 5950 \| 0.0 \| - \|
	\| 19.2926 \| 6000 \| 0.0 \| - \|
	\| 19.4534 \| 6050 \| 0.0 \| - \|
	\| 19.6141 \| 6100 \| 0.0 \| - \|
	\| 19.7749 \| 6150 \| 0.0 \| - \|
	\| 19.9357 \| 6200 \| 0.0 \| - \|
	\| 20.0965 \| 6250 \| 0.0 \| - \|
	\| 20.2572 \| 6300 \| 0.0 \| - \|
	\| 20.4180 \| 6350 \| 0.0 \| - \|
	\| 20.5788 \| 6400 \| 0.0 \| - \|
	\| 20.7395 \| 6450 \| 0.0 \| - \|
	\| 20.9003 \| 6500 \| 0.0 \| - \|
	\| 21.0611 \| 6550 \| 0.0 \| - \|
	\| 21.2219 \| 6600 \| 0.0 \| - \|
	\| 21.3826 \| 6650 \| 0.0 \| - \|
	\| 21.5434 \| 6700 \| 0.0 \| - \|
	\| 21.7042 \| 6750 \| 0.0 \| - \|
	\| 21.8650 \| 6800 \| 0.0 \| - \|
	\| 22.0257 \| 6850 \| 0.0 \| - \|
	\| 22.1865 \| 6900 \| 0.0 \| - \|
	\| 22.3473 \| 6950 \| 0.0 \| - \|
	\| 22.5080 \| 7000 \| 0.0 \| - \|
	\| 22.6688 \| 7050 \| 0.0 \| - \|
	\| 22.8296 \| 7100 \| 0.0 \| - \|
	\| 22.9904 \| 7150 \| 0.0 \| - \|
	\| 23.1511 \| 7200 \| 0.0 \| - \|
	\| 23.3119 \| 7250 \| 0.0 \| - \|
	\| 23.4727 \| 7300 \| 0.0 \| - \|
	\| 23.6334 \| 7350 \| 0.0 \| - \|
	\| 23.7942 \| 7400 \| 0.0 \| - \|
	\| 23.9550 \| 7450 \| 0.0 \| - \|
	\| 24.1158 \| 7500 \| 0.0 \| - \|
	\| 24.2765 \| 7550 \| 0.0 \| - \|
	\| 24.4373 \| 7600 \| 0.0 \| - \|
	\| 24.5981 \| 7650 \| 0.0 \| - \|
	\| 24.7588 \| 7700 \| 0.0 \| - \|
	\| 24.9196 \| 7750 \| 0.0 \| - \|
	\| 25.0804 \| 7800 \| 0.0 \| - \|
	\| 25.2412 \| 7850 \| 0.0 \| - \|
	\| 25.4019 \| 7900 \| 0.0 \| - \|
	\| 25.5627 \| 7950 \| 0.0 \| - \|
	\| 25.7235 \| 8000 \| 0.0 \| - \|
	\| 25.8842 \| 8050 \| 0.0 \| - \|
	\| 26.0450 \| 8100 \| 0.0 \| - \|
	\| 26.2058 \| 8150 \| 0.0 \| - \|
	\| 26.3666 \| 8200 \| 0.0 \| - \|
	\| 26.5273 \| 8250 \| 0.0 \| - \|
	\| 26.6881 \| 8300 \| 0.0 \| - \|
	\| 26.8489 \| 8350 \| 0.0 \| - \|
	\| 27.0096 \| 8400 \| 0.0 \| - \|
	\| 27.1704 \| 8450 \| 0.0 \| - \|
	\| 27.3312 \| 8500 \| 0.0 \| - \|
	\| 27.4920 \| 8550 \| 0.0 \| - \|
	\| 27.6527 \| 8600 \| 0.0 \| - \|
	\| 27.8135 \| 8650 \| 0.0 \| - \|
	\| 27.9743 \| 8700 \| 0.0 \| - \|
	\| 28.1350 \| 8750 \| 0.0 \| - \|
	\| 28.2958 \| 8800 \| 0.0 \| - \|
	\| 28.4566 \| 8850 \| 0.0 \| - \|
	\| 28.6174 \| 8900 \| 0.0 \| - \|
	\| 28.7781 \| 8950 \| 0.0 \| - \|
	\| 28.9389 \| 9000 \| 0.0 \| - \|
	\| 29.0997 \| 9050 \| 0.0 \| - \|
	\| 29.2605 \| 9100 \| 0.0 \| - \|
	\| 29.4212 \| 9150 \| 0.0 \| - \|
	\| 29.5820 \| 9200 \| 0.0 \| - \|
	\| 29.7428 \| 9250 \| 0.0 \| - \|
	\| 29.9035 \| 9300 \| 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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