master_cate_bc24 / README.md

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	---
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: 쁘리엘르리틀 침대 방수패드 신생아 아기 유아 11.쿠마 순면 유아 방수패드_소형 출산/육아 > 유아침구 > 방수요
	- text: 꽈배기 가드 침대 아기 범퍼 뒤집기방지 충돌방지 바디필로우 쿠션 G_싱글 스틱 5 미터 이상 직경 약 5cm 출산/육아 > 유아침구
	> 기타유아침구
	- text: 신생아 배넷저고리 담요 랩 모자 세트, 조절 가능한 침낭 모슬린 담요, 면 소재, 0-6M, 세 128973 꽃 128973 별 출산/육아
	> 유아침구 > 아기이불/요/패드
	- text: 순면 모달 귀여운 어린이집 낮잠이불 풀세트 30.디노디노 낮잠패드_패드단품 출산/육아 > 유아침구 > 낮잠이불
	- text: '[모던하우스]드림베어 플란넬 극세사 베개커버 40x60 아이보리베이지/CZ0323075 출산/육아 > 유아침구 > 유아베개'
	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: 9 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 \|
	\|:------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 1.0 \| <ul><li>'[해외] 육아용품 국경 새 Swaddling 침낭 안티 점프 퀼트 사계절 신생아 긴팔 손 M (월 월) (크기 큰 겨울)_라이트 그린 (포켓) 출산/육아 > 유아침구 > 겉싸개'</li><li>'조아뜨 아기 여름 겉싸개 신생아 여름용 누비 봄 겉싸보 C 플라워겉싸개(여름용)+턱받이 출산/육아 > 유아침구 > 겉싸개'</li><li>'[슈슈비] 가을 신생아 속싸개 블랭킷 스타양면속싸개 출산/육아 > 유아침구 > 겉싸개'</li></ul> \|
	\| 8.0 \| <ul><li>'신생아 유아 돌 아기 어린이집 낮잠 베개 배게 송송필로우 미니고미 출산/육아 > 유아침구 > 유아베개'</li><li>'이몽 가슴안정 좁쌀 태열베개 4구획/6구획 (모로반사,신생아,아기,냉감) 피그먼트 누빔(4구획)_그레이 출산/육아 > 유아침구 > 유아베개'</li><li>'굿나잇 베이비 꿀잠블랭킷 Hug me Small_아이보리 출산/육아 > 유아침구 > 유아베개'</li></ul> \|
	\| 6.0 \| <ul><li>'[엘라바] 특허받은 무소음 스와들 스트랩 2개세트 에어로쿨(화이트)S_면매쉬(화이트)M 출산/육아 > 유아침구 > 속싸개'</li><li>'리코타입 속싸개 오리지널 얇은 2벌 세트 아기양말 제공 나비잠 스와들 신생아 출산준비 뉴본+뉴본플러스_피치_베이지 출산/육아 > 유아침구 > 속싸개'</li><li>'핀츠빈 스와들 태열 신생아 오가닉 나비잠 속싸개 라이트_화이트_M 출산/육아 > 유아침구 > 속싸개'</li></ul> \|
	\| 7.0 \| <ul><li>'어린이용 잠옷 침낭 집에서 입을 수 있는 유아용 코튼 피그 베이비_150 cm 출산/육아 > 유아침구 > 아기이불/요/패드'</li><li>'요루거즈 블랭킷 아기 유아 신생아 어린이집 낮잠 이불 유모차 2중(85x110)_체리라마 출산/육아 > 유아침구 > 아기이불/요/패드'</li><li>'[갤러리아] [리틀아카이브][크레므앙팡] 텔라 모달 낮잠패드세트(J)+베개솜(한화갤러리아㈜ 센터시티) 블루 출산/육아 > 유아침구 > 아기이불/요/패드'</li></ul> \|
	\| 5.0 \| <ul><li>'꼬마 이불가방 58X40cm 1p 이불가방 아기 어린이집 그린 출산/육아 > 유아침구 > 보낭/슬리핑백'</li><li>'베이비[블루독]클라우드퀼팅슬리핑가운 41A7000902 M 출산/육아 > 유아침구 > 보낭/슬리핑백'</li><li>'오가닉맘 /필로스슬리핑백(MIFLSL03) 크림_m 출산/육아 > 유아침구 > 보낭/슬리핑백'</li></ul> \|
	\| 3.0 \| <ul><li>'아망떼 순면 모달 귀여운 어린이집 낮잠이불 모음 51.레오파드 범퍼 핑크_6개세트 출산/육아 > 유아침구 > 낮잠이불'</li><li>'어린이집 낮잠이불 세트 순면 워싱 마이크로 자수 양면 아기낮잠이불 소프트피치_미드나잇블루_패드+베개(고급일반솜) 출산/육아 > 유아침구 > 낮잠이불'</li><li>'분리형낮잠이불 어린이집 순면 토끼 아기 낮잠 이불 베개 20X40(솜포함)_블루 출산/육아 > 유아침구 > 낮잠이불'</li></ul> \|
	\| 4.0 \| <ul><li>'[리틀아카이브](신세계천안아산점)크레므앙팡] 치포 밍크 유모차 블랭킷 B(75X100) 핑크 출산/육아 > 유아침구 > 방수요'</li><li>'디유 순면 유아 방수패드 소형 대형 대형_피치 출산/육아 > 유아침구 > 방수요'</li><li>'뮤라 에어와플방수요 방수패드 M 미디움사이즈(100x130) 실버클라우드 출산/육아 > 유아침구 > 방수요'</li></ul> \|
	\| 0.0 \| <ul><li>'[DIY+동영상]뚱이 코튼 Big 코바늘 뜨개질 손뜨개인형 027 화이트 출산/육아 > 유아침구 > DIY아기용품'</li><li>'[갤러리아] (출산선물)루팅타올(APA-10901)선물포장+손수건(한화갤러리아㈜ 진주점) 아이보리 출산/육아 > 유아침구 > DIY아기용품'</li><li>'릴리팟 이유식 냄비/통3중/편수냄비/밀크팬/이유식조리도구/국민이유식냄비/이유식조리도구 이유식냄비 출산/육아 > 유아침구 > DIY아기용품'</li></ul> \|
	\| 2.0 \| <ul><li>'마롤로뜨 아기 침대 범퍼가드 신생아 쿠션 바디필로우 이케아 벨라 보니 스마트 쁘띠라뺑 내추럴아이보리_이케아S (소형) 출산/육아 > 유아침구 > 기타유아침구'</li><li>'[현대백화점][M밍크뮤M7]33W70ABS01 체리모달낮잠이불SET (33W70ABS01)/유아잡화/출산준비 [00001] 피치/FREE(85X115cm) 출산/육아 > 유아침구 > 기타유아침구'</li><li>'마롤로뜨 100수 누빔 자수 아기 바디필로우 죽부인 베개 롱쿠션 애착베개 11.자수 누빔 아기죽부인_상큼체리 출산/육아 > 유아침구 > 기타유아침구'</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_bc24")
	# Run inference
	preds = model("순면 모달 귀여운 어린이집 낮잠이불 풀세트 30.디노디노 낮잠패드_패드단품 출산/육아 > 유아침구 > 낮잠이불")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:--------\|:----\|
	\| Word count \| 7 \| 15.1397 \| 27 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0.0 \| 70 \|
	\| 1.0 \| 70 \|
	\| 2.0 \| 70 \|
	\| 3.0 \| 70 \|
	\| 4.0 \| 70 \|
	\| 5.0 \| 70 \|
	\| 6.0 \| 70 \|
	\| 7.0 \| 70 \|
	\| 8.0 \| 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0081 \| 1 \| 0.517 \| - \|
	\| 0.4032 \| 50 \| 0.4962 \| - \|
	\| 0.8065 \| 100 \| 0.4906 \| - \|
	\| 1.2097 \| 150 \| 0.454 \| - \|
	\| 1.6129 \| 200 \| 0.0667 \| - \|
	\| 2.0161 \| 250 \| 0.0003 \| - \|
	\| 2.4194 \| 300 \| 0.0001 \| - \|
	\| 2.8226 \| 350 \| 0.0001 \| - \|
	\| 3.2258 \| 400 \| 0.0 \| - \|
	\| 3.6290 \| 450 \| 0.0 \| - \|
	\| 4.0323 \| 500 \| 0.0 \| - \|
	\| 4.4355 \| 550 \| 0.0 \| - \|
	\| 4.8387 \| 600 \| 0.0 \| - \|
	\| 5.2419 \| 650 \| 0.0 \| - \|
	\| 5.6452 \| 700 \| 0.0 \| - \|
	\| 6.0484 \| 750 \| 0.0 \| - \|
	\| 6.4516 \| 800 \| 0.0 \| - \|
	\| 6.8548 \| 850 \| 0.0 \| - \|
	\| 7.2581 \| 900 \| 0.0 \| - \|
	\| 7.6613 \| 950 \| 0.0 \| - \|
	\| 8.0645 \| 1000 \| 0.0 \| - \|
	\| 8.4677 \| 1050 \| 0.0 \| - \|
	\| 8.8710 \| 1100 \| 0.0 \| - \|
	\| 9.2742 \| 1150 \| 0.0 \| - \|
	\| 9.6774 \| 1200 \| 0.0 \| - \|
	\| 10.0806 \| 1250 \| 0.0 \| - \|
	\| 10.4839 \| 1300 \| 0.0 \| - \|
	\| 10.8871 \| 1350 \| 0.0 \| - \|
	\| 11.2903 \| 1400 \| 0.0 \| - \|
	\| 11.6935 \| 1450 \| 0.0 \| - \|
	\| 12.0968 \| 1500 \| 0.0 \| - \|
	\| 12.5 \| 1550 \| 0.0 \| - \|
	\| 12.9032 \| 1600 \| 0.0 \| - \|
	\| 13.3065 \| 1650 \| 0.0 \| - \|
	\| 13.7097 \| 1700 \| 0.0 \| - \|
	\| 14.1129 \| 1750 \| 0.0 \| - \|
	\| 14.5161 \| 1800 \| 0.0 \| - \|
	\| 14.9194 \| 1850 \| 0.0 \| - \|
	\| 15.3226 \| 1900 \| 0.0 \| - \|
	\| 15.7258 \| 1950 \| 0.0 \| - \|
	\| 16.1290 \| 2000 \| 0.0 \| - \|
	\| 16.5323 \| 2050 \| 0.0 \| - \|
	\| 16.9355 \| 2100 \| 0.0 \| - \|
	\| 17.3387 \| 2150 \| 0.0 \| - \|
	\| 17.7419 \| 2200 \| 0.0 \| - \|
	\| 18.1452 \| 2250 \| 0.0 \| - \|
	\| 18.5484 \| 2300 \| 0.0 \| - \|
	\| 18.9516 \| 2350 \| 0.0 \| - \|
	\| 19.3548 \| 2400 \| 0.0 \| - \|
	\| 19.7581 \| 2450 \| 0.0 \| - \|
	\| 20.1613 \| 2500 \| 0.0 \| - \|
	\| 20.5645 \| 2550 \| 0.0 \| - \|
	\| 20.9677 \| 2600 \| 0.0 \| - \|
	\| 21.3710 \| 2650 \| 0.0 \| - \|
	\| 21.7742 \| 2700 \| 0.0 \| - \|
	\| 22.1774 \| 2750 \| 0.0 \| - \|
	\| 22.5806 \| 2800 \| 0.0 \| - \|
	\| 22.9839 \| 2850 \| 0.0 \| - \|
	\| 23.3871 \| 2900 \| 0.0 \| - \|
	\| 23.7903 \| 2950 \| 0.0 \| - \|
	\| 24.1935 \| 3000 \| 0.0 \| - \|
	\| 24.5968 \| 3050 \| 0.0 \| - \|
	\| 25.0 \| 3100 \| 0.0 \| - \|
	\| 25.4032 \| 3150 \| 0.0 \| - \|
	\| 25.8065 \| 3200 \| 0.0 \| - \|
	\| 26.2097 \| 3250 \| 0.0 \| - \|
	\| 26.6129 \| 3300 \| 0.0 \| - \|
	\| 27.0161 \| 3350 \| 0.0 \| - \|
	\| 27.4194 \| 3400 \| 0.0 \| - \|
	\| 27.8226 \| 3450 \| 0.0 \| - \|
	\| 28.2258 \| 3500 \| 0.0 \| - \|
	\| 28.6290 \| 3550 \| 0.0 \| - \|
	\| 29.0323 \| 3600 \| 0.0 \| - \|
	\| 29.4355 \| 3650 \| 0.0 \| - \|
	\| 29.8387 \| 3700 \| 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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