master_cate_bc16 / README.md

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	---
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: 하이라이크 유모차장갑 핸드머프 방한장갑 블랙체스 출산/육아 > 유모차 > 유모차용품 > 기타유모차용품
	- text: 대통 K-Express 대문 출입문 게이트 스텐 펜스 접이식 정문 자바라 절연 텔레스코픽 튜브 울타리 전기 안전 건설 이동식 난간 유치원
	격리 AB.플라스틱 0.96 높이X2.5 긴 빨간색 출산/육아 > 유모차 > 유모차용품 > 유모차보호대/안전바
	- text: HABBY 휴대용/절충형/디럭스 유모차 누빔 방한커버 방풍커버 04.하삐 휴대용 방한커버 출산/육아 > 유모차 > 유모차용품 > 유모차커버
	- text: 올겟쇼핑 나이스 유모차 컵홀더 스마트폰거치대 출산/육아 > 유모차 > 유모차용품 > 유모차홀더
	- text: NEW 오이스터3 플러스 유모차 클래식 에디션 브라운 샌드 베이지 디럭스 절충형 8종선물 오이스터3 플러스 에디션_플러스 샴페인샌드(8종선물)
	출산/육아 > 유모차 > 절충형/디럭스형
	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>'스토케 베이비젠 요요2 프리미엄 휴대용 유모차 화이트_올리브 출산/육아 > 유모차 > 초경량/휴대용'</li><li>'이지폴드3 하이브리드 초경량 오토폴딩 휴대용 유모차 - 아미그린 크림베이지 출산/육아 > 유모차 > 초경량/휴대용'</li><li>'르클레르 인플루언서 에디션 휴대용 유모차 - 샌드쇼콜라 [선물6종] 제트블랙 출산/육아 > 유모차 > 초경량/휴대용'</li></ul> \|
	\| 2.0 \| <ul><li>'유아목베개 유모차베개 카시트 목쿠션 01_딸기옐로우 매쉬목베개 출산/육아 > 유모차 > 유모차용품 > 유모차목쿠션/블랭킷'</li><li>'돗투돗 소프트 유모차 라이너 유모차 시트 신생아 시트 100수소프트라이너_베이지덕 출산/육아 > 유모차 > 유모차용품 > 유모차시트'</li><li>'아기 방수 겨울용 유모차 침낭 따뜻한 발싸개 범용 풋커버 풋머프 슬립 색 봉투 15=6-36mGray 출산/육아 > 유모차 > 유모차용품 > 유모차시트'</li></ul> \|
	\| 3.0 \| <ul><li>'[디럭스]부가부 폭스5 그래파이트 섀시/미드나이트 블랙 시트패브릭_100052034 포레스트 그린 출산/육아 > 유모차 > 절충형/디럭스형'</li><li>'마마스앤파파스 절충형 유모차 스트라다 - 그레이 아이비_컵홀더/모기장/레인커버/맘노코방풍커버 출산/육아 > 유모차 > 절충형/디럭스형'</li><li>'[판매] 싸이벡스 프리암 맨하탄그레이 디럭스 유모차 비동의_프리암 맨하탄그레이 출산/육아 > 유모차 > 절충형/디럭스형'</li></ul> \|
	\| 0.0 \| <ul><li>'스토케 베이비젠 요요2 프리미엄 휴대용 유모차(커넥트 포함) 쌍둥이 유모차 블랙_에어프랑스 블루_타프 출산/육아 > 유모차 > 쌍둥이용'</li><li>'형제유모차 쌍둥이유모차 더블시트 디럭스형 폴딩 색상 유형 및 구성13 출산/육아 > 유모차 > 쌍둥이용'</li><li>'트윈유모차 2인용 휴대용유모카 디럭스형 트라이크 디자인E 출산/육아 > 유모차 > 쌍둥이용'</li></ul> \|
	\| 1.0 \| <ul><li>'원클릭으로 앉거나 누울 수 있는 유모차, 초경량 충격흡수 접이식 간편 신생아 유모차 12 주력 모델 기질 파란색 원클릭으로 차 출산/육아 > 유모차 > 유모차/카시트세트'</li><li>'어린이카시트 쥬니어카시트 유아용 휴대용 간단한 잠금 해제 도구 자동차 좌석 키 장치, 안 03 C 출산/육아 > 유모차 > 유모차/카시트세트'</li><li>'유모차 유물, 경량 접이식 어린이 유모차, 양방향 유모차, 아기 산책 시 원클릭 접이식 15 카라멜 컬러 자동차 매트시트 수납백팩 출산/육아 > 유모차 > 유모차/카시트세트'</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_bc16")
	# Run inference
	preds = model("올겟쇼핑 나이스 유모차 컵홀더 스마트폰거치대 출산/육아 > 유모차 > 유모차용품 > 유모차홀더")
	```

	<!--
	### Downstream Use

	List how someone could finetune this model on their own dataset.
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	### Out-of-Scope Use

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:--------\|:----\|
	\| Word count \| 7 \| 16.2771 \| 32 \|

	\| 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 \| 0.4996 \| - \|
	\| 1.4493 \| 100 \| 0.4912 \| - \|
	\| 2.1739 \| 150 \| 0.2633 \| - \|
	\| 2.8986 \| 200 \| 0.0252 \| - \|
	\| 3.6232 \| 250 \| 0.0001 \| - \|
	\| 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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