master_cate_fi11 / README.md

Push model using huggingface_hub.

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	---
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: 바운티풀 프리미엄 코마사 사틴면 호텔 이불커버 Q 가구/인테리어>침구단품>이불커버
	- text: 쇼파커버 사계절 담요 블랭킷 캠핑 이불 차박 대형 러그 가구/인테리어>침구단품>담요
	- text: 플로라 시어서커 리플 여름 홑이불 SS 가구/인테리어>침구단품>홑이불
	- text: 아이리스 포르토MT 모달 워싱 스프레드 Q 가구/인테리어>침구단품>스프레드
	- text: 모던하우스 마이호텔 여름 모달혼방 고밀도워싱 차렵이불 S 가구/인테리어>침구단품>차렵이불
	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: 13 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 \|
	\|:------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 0.0 \| <ul><li>'바보사랑 여기있소 침대시트 고정밴드 이불클립 8개입 2세트 가구/인테리어>침구단품>기타침구단품'</li><li>'밀림방지 원터치 침구고정핀 이불 침대시트 소파커버 고정클립 가구/인테리어>침구단품>기타침구단품'</li><li>'두잉썸 고밀도 80수 베개커버 가구/인테리어>침구단품>기타침구단품'</li></ul> \|
	\| 9.0 \| <ul><li>'템퍼 프로 플러스 미디엄 타퍼 가구/인테리어>침구단품>토퍼'</li><li>'아망떼 뉴데이즈 세미마이크로 고정밴드 토퍼SS Q K 가구/인테리어>침구단품>토퍼'</li><li>'아이스 쿨 매트리스 토퍼 가구/인테리어>침구단품>토퍼'</li></ul> \|
	\| 10.0 \| <ul><li>'숙면연구소 베이직 토퍼매트리스 전용 일자형 방수속커버 Q 가구/인테리어>침구단품>토퍼커버'</li><li>'아망떼 리틀캣 알러지케어 요커버SS Q 가구/인테리어>침구단품>토퍼커버'</li><li>'두꺼운 퀼트 매트리스 커버 코튼 프로텍터 부드러운 진드기 토퍼 저자극성 공기 투과성 침대 가구/인테리어>침구단품>토퍼커버'</li></ul> \|
	\| 11.0 \| <ul><li>'극세사 겨울 스프레드 침대패드 토퍼 가구/인테리어>침구단품>패드>더블/퀸/킹패드'</li><li>'헬로우슬립 부드럽고 따뜻한 겨울 밀크 극세사 고정밴딩 침대패드 Q K - 가구/인테리어>침구단품>패드>싱글/슈퍼싱글패드'</li><li>'파르페by알레르망 스노우쿨 듀라론 냉감 3중직 패드 Q 가구/인테리어>침구단품>패드>더블/퀸/킹패드'</li></ul> \|
	\| 1.0 \| <ul><li>'플란넬 담요 겨울 무릎 캠핑 극세사 미니 대형 가구/인테리어>침구단품>담요'</li><li>'대형 캠핑 감성 블랭킷 담요 카페트 3종 가구/인테리어>침구단품>담요'</li><li>'방콕 극세사 겨울 잠옷 입는담요 가구/인테리어>침구단품>담요'</li></ul> \|
	\| 2.0 \| <ul><li>'먼지없는 부드러운 포인트라인 침대패드 슈퍼싱글 가구/인테리어>침구단품>매트/침대커버>싱글/슈퍼싱글침대커버'</li><li>'머쉬룸 매트리스커버 방수 침대 커버 순면 매트 시트 오가닉코튼 싱글 퀸 킹 누빔 알러지케어 K킹 가구/인테리어>침구단품>매트/침대커버>싱글/슈퍼싱글매트커버'</li><li>'인테리어포스터 벽장식 패브릭 대형 오아시스 중 집꾸미기 인테리어소품 포스터 가구/인테리어>침구단품>매트/침대커버>싱글/슈퍼싱글침대커버'</li></ul> \|
	\| 8.0 \| <ul><li>'모던하우스 슈퍼쿨 다니엘 쿨링 매쉬 이불 S 가구/인테리어>침구단품>차렵이불'</li><li>'세사 버터링 알러지케어 차렵이불 S 가구/인테리어>침구단품>차렵이불'</li><li>'알레르망 플레인 80수 고밀도 바이오워싱 호텔식 무봉제 차렵이불 S/SS 가구/인테리어>침구단품>차렵이불'</li></ul> \|
	\| 3.0 \| <ul><li>'오너클랜 캐릭터 롤카펫 극세사 무릎담요 선인장 어린이 학생 가구/인테리어>침구단품>무릎담요'</li><li>'알쏭달쏭 캐치티니핑 극세사 로얄핑 담요 100x70 집순이 블랭킷 차박 학생 사무실 캐릭터담요 가구/인테리어>침구단품>무릎담요'</li><li>'조구만 스튜디오 무지개 담요 가구/인테리어>침구단품>무릎담요'</li></ul> \|
	\| 4.0 \| <ul><li>'이솔홈 이음 피그먼트 순면 모달벨벳 이불 패드 스프레드 가구/인테리어>침구단품>스프레드'</li><li>'차렵이불세트 60수 아사 순면 스프레드 S 싱글 가구/인테리어>침구단품>스프레드'</li><li>'소오미홈 에코 벨로아 모달 줄누비 양면 싱글 퀸 킹패드 가구/인테리어>침구단품>스프레드'</li></ul> \|
	\| 7.0 \| <ul><li>'더크린 라이너 세미마이크로 온수매트커버 Q그레이 가구/인테리어>침구단품>전기매트커버'</li><li>'방수 두꺼운 보호대 커버 탄성 밴드 고정 시트 가정용 퀼트 패드 침대 가구/인테리어>침구단품>전기매트커버'</li><li>'매트 커버 꽃 시트 3색 지퍼 리스 용 단품 온수 장판 u자 전기 침대 가구/인테리어>침구단품>전기매트커버'</li></ul> \|
	\| 5.0 \| <ul><li>'라이크홈 에코항균 누빔 요커버 S 가구/인테리어>침구단품>요>요커버'</li><li>'올리비아데코 베리메리 60수 아사 요세트 Q 가구/인테리어>침구단품>요>요세트'</li><li>'더블샵 3단 접이식 요매트 2인용 매트리스 가구/인테리어>침구단품>요>요세트'</li></ul> \|
	\| 12.0 \| <ul><li>'파르페 썸머 뱀부서커 여름 홑이불 MINI 가구/인테리어>침구단품>홑이불'</li><li>'엘르파리 엘르 파리 포엠 냉감 홑이불 S 가구/인테리어>침구단품>홑이불'</li><li>'기라로쉬 인견 여름홑이불 160x200 가구/인테리어>침구단품>홑이불'</li></ul> \|
	\| 6.0 \| <ul><li>'아이리스 릴라WH 면 홑겹 이불커버 Q 가구/인테리어>침구단품>이불커버'</li><li>'한파용 80수 폴란드 구스 극세사 알러지케어 이불커버 -싱글 가구/인테리어>침구단품>이불커버'</li><li>'사슴 이불 동물 베개커버 꽃밭 이불커버 귀여운 침구-6 가구/인테리어>침구단품>이불커버'</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_fi11")
	# Run inference
	preds = model("플로라 시어서커 리플 여름 홑이불 SS 가구/인테리어>침구단품>홑이불")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 3 \| 8.8067 \| 23 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0.0 \| 70 \|
	\| 1.0 \| 70 \|
	\| 2.0 \| 70 \|
	\| 3.0 \| 70 \|
	\| 4.0 \| 70 \|
	\| 5.0 \| 50 \|
	\| 6.0 \| 70 \|
	\| 7.0 \| 70 \|
	\| 8.0 \| 70 \|
	\| 9.0 \| 70 \|
	\| 10.0 \| 70 \|
	\| 11.0 \| 70 \|
	\| 12.0 \| 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0057 \| 1 \| 0.5104 \| - \|
	\| 0.2874 \| 50 \| 0.4986 \| - \|
	\| 0.5747 \| 100 \| 0.3956 \| - \|
	\| 0.8621 \| 150 \| 0.1871 \| - \|
	\| 1.1494 \| 200 \| 0.0555 \| - \|
	\| 1.4368 \| 250 \| 0.017 \| - \|
	\| 1.7241 \| 300 \| 0.0073 \| - \|
	\| 2.0115 \| 350 \| 0.0015 \| - \|
	\| 2.2989 \| 400 \| 0.0003 \| - \|
	\| 2.5862 \| 450 \| 0.0002 \| - \|
	\| 2.8736 \| 500 \| 0.0001 \| - \|
	\| 3.1609 \| 550 \| 0.0001 \| - \|
	\| 3.4483 \| 600 \| 0.0001 \| - \|
	\| 3.7356 \| 650 \| 0.0001 \| - \|
	\| 4.0230 \| 700 \| 0.0001 \| - \|
	\| 4.3103 \| 750 \| 0.0001 \| - \|
	\| 4.5977 \| 800 \| 0.0001 \| - \|
	\| 4.8851 \| 850 \| 0.0001 \| - \|
	\| 5.1724 \| 900 \| 0.0 \| - \|
	\| 5.4598 \| 950 \| 0.0 \| - \|
	\| 5.7471 \| 1000 \| 0.0 \| - \|
	\| 6.0345 \| 1050 \| 0.0 \| - \|
	\| 6.3218 \| 1100 \| 0.0 \| - \|
	\| 6.6092 \| 1150 \| 0.0 \| - \|
	\| 6.8966 \| 1200 \| 0.0 \| - \|
	\| 7.1839 \| 1250 \| 0.0 \| - \|
	\| 7.4713 \| 1300 \| 0.0001 \| - \|
	\| 7.7586 \| 1350 \| 0.0 \| - \|
	\| 8.0460 \| 1400 \| 0.0 \| - \|
	\| 8.3333 \| 1450 \| 0.0 \| - \|
	\| 8.6207 \| 1500 \| 0.0 \| - \|
	\| 8.9080 \| 1550 \| 0.0 \| - \|
	\| 9.1954 \| 1600 \| 0.0 \| - \|
	\| 9.4828 \| 1650 \| 0.0 \| - \|
	\| 9.7701 \| 1700 \| 0.0 \| - \|
	\| 10.0575 \| 1750 \| 0.0 \| - \|
	\| 10.3448 \| 1800 \| 0.0 \| - \|
	\| 10.6322 \| 1850 \| 0.0 \| - \|
	\| 10.9195 \| 1900 \| 0.0 \| - \|
	\| 11.2069 \| 1950 \| 0.0 \| - \|
	\| 11.4943 \| 2000 \| 0.0 \| - \|
	\| 11.7816 \| 2050 \| 0.0 \| - \|
	\| 12.0690 \| 2100 \| 0.0 \| - \|
	\| 12.3563 \| 2150 \| 0.0 \| - \|
	\| 12.6437 \| 2200 \| 0.0 \| - \|
	\| 12.9310 \| 2250 \| 0.0 \| - \|
	\| 13.2184 \| 2300 \| 0.0 \| - \|
	\| 13.5057 \| 2350 \| 0.0 \| - \|
	\| 13.7931 \| 2400 \| 0.0 \| - \|
	\| 14.0805 \| 2450 \| 0.0 \| - \|
	\| 14.3678 \| 2500 \| 0.0 \| - \|
	\| 14.6552 \| 2550 \| 0.0 \| - \|
	\| 14.9425 \| 2600 \| 0.0 \| - \|
	\| 15.2299 \| 2650 \| 0.0 \| - \|
	\| 15.5172 \| 2700 \| 0.0 \| - \|
	\| 15.8046 \| 2750 \| 0.0 \| - \|
	\| 16.0920 \| 2800 \| 0.0 \| - \|
	\| 16.3793 \| 2850 \| 0.0 \| - \|
	\| 16.6667 \| 2900 \| 0.0 \| - \|
	\| 16.9540 \| 2950 \| 0.0 \| - \|
	\| 17.2414 \| 3000 \| 0.0 \| - \|
	\| 17.5287 \| 3050 \| 0.0 \| - \|
	\| 17.8161 \| 3100 \| 0.0 \| - \|
	\| 18.1034 \| 3150 \| 0.0 \| - \|
	\| 18.3908 \| 3200 \| 0.0 \| - \|
	\| 18.6782 \| 3250 \| 0.0 \| - \|
	\| 18.9655 \| 3300 \| 0.0 \| - \|
	\| 19.2529 \| 3350 \| 0.0 \| - \|
	\| 19.5402 \| 3400 \| 0.0 \| - \|
	\| 19.8276 \| 3450 \| 0.0 \| - \|
	\| 20.1149 \| 3500 \| 0.0 \| - \|
	\| 20.4023 \| 3550 \| 0.0 \| - \|
	\| 20.6897 \| 3600 \| 0.0 \| - \|
	\| 20.9770 \| 3650 \| 0.0 \| - \|
	\| 21.2644 \| 3700 \| 0.0 \| - \|
	\| 21.5517 \| 3750 \| 0.0 \| - \|
	\| 21.8391 \| 3800 \| 0.0 \| - \|
	\| 22.1264 \| 3850 \| 0.0 \| - \|
	\| 22.4138 \| 3900 \| 0.0 \| - \|
	\| 22.7011 \| 3950 \| 0.0 \| - \|
	\| 22.9885 \| 4000 \| 0.0 \| - \|
	\| 23.2759 \| 4050 \| 0.0 \| - \|
	\| 23.5632 \| 4100 \| 0.0 \| - \|
	\| 23.8506 \| 4150 \| 0.0 \| - \|
	\| 24.1379 \| 4200 \| 0.0 \| - \|
	\| 24.4253 \| 4250 \| 0.0 \| - \|
	\| 24.7126 \| 4300 \| 0.0 \| - \|
	\| 25.0 \| 4350 \| 0.0 \| - \|
	\| 25.2874 \| 4400 \| 0.0 \| - \|
	\| 25.5747 \| 4450 \| 0.0 \| - \|
	\| 25.8621 \| 4500 \| 0.0 \| - \|
	\| 26.1494 \| 4550 \| 0.0 \| - \|
	\| 26.4368 \| 4600 \| 0.0 \| - \|
	\| 26.7241 \| 4650 \| 0.0 \| - \|
	\| 27.0115 \| 4700 \| 0.0 \| - \|
	\| 27.2989 \| 4750 \| 0.0 \| - \|
	\| 27.5862 \| 4800 \| 0.0 \| - \|
	\| 27.8736 \| 4850 \| 0.0 \| - \|
	\| 28.1609 \| 4900 \| 0.0 \| - \|
	\| 28.4483 \| 4950 \| 0.0 \| - \|
	\| 28.7356 \| 5000 \| 0.0 \| - \|
	\| 29.0230 \| 5050 \| 0.0 \| - \|
	\| 29.3103 \| 5100 \| 0.0 \| - \|
	\| 29.5977 \| 5150 \| 0.0 \| - \|
	\| 29.8851 \| 5200 \| 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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	Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.
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	## Model Card Contact

	Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.
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