master_cate_sl20 / README.md

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	---
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: 1477844 ScubaPro Magnetic Octo Holder Tank by Scubapro 스포츠/레저>스킨스쿠버>옥토퍼스
	- text: 브로브 수영랜턴 고급형 스노쿨링 잠수후레쉬 CREE 스포츠/레저>스킨스쿠버>수중전등
	- text: 수초대 수초 제거 낫 칼 갈퀴 낚시 긴 장대 막대 작살 스포츠/레저>스킨스쿠버>수중총/작살
	- text: 아팩스 아펙스Apeks WTX Tek 포켓 작은 메시 포켓 스포츠/레저>스킨스쿠버>부력재킷
	- text: 홀리스 부력조절기 하네스 웨이트 STA WEIGHT 스포츠/레저>스킨스쿠버>부력재킷
	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: 14 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>'마레스 스마트 다이브 컴퓨터 스포츠/레저>스킨스쿠버>게이지'</li><li>'2pcs 스쿠버 다이빙 고압 HP 스위블 스풀 WO SPG용 게이지 스포츠/레저>스킨스쿠버>게이지'</li><li>'순토 SUUNTO PM-5 클리노미터 레귤러 pm-5 360 SS011096010 스포츠/레저>스킨스쿠버>게이지'</li></ul> \|
	\| 3.0 \| <ul><li>'메이트로닉스용 클라이밍 링 돌고래 M200 M400 M500 수영장 로봇 휠 커버 클라이밍 링 교체 4 개 6101611-R4 스포츠/레저>스킨스쿠버>기타스킨스쿠버용품'</li><li>'스테인리스강 수중 프로브 내구성 딩 스틱 다이빙 포인터 액세서리 스포츠/레저>스킨스쿠버>기타스킨스쿠버용품'</li><li>'DOUBLE K 더블케이 더블케이 재규어R 오로라 BK- 다이빙마스크 스포츠/레저>스킨스쿠버>기타스킨스쿠버용품'</li></ul> \|
	\| 7.0 \| <ul><li>'스쿠버프로 스쿠바프로 레귤레이터호흡기 MK11 C370 스포츠/레저>스킨스쿠버>레귤레이터'</li><li>'DIDEEP 스쿠버 다이빙 산소탱크 미니 산소통 호흡기 입문자 스노클링 탱크 스포츠/레저>스킨스쿠버>레귤레이터'</li><li>'스쿠버프로 스쿠바프로 레귤레이터 호흡기 EVO S620 X-TI MK25T 스포츠/레저>스킨스쿠버>레귤레이터'</li></ul> \|
	\| 5.0 \| <ul><li>'다이빙헤어밴드 스쿠버 후드 잠수 프리다이빙 머리띠 스포츠/레저>스킨스쿠버>다이빙슈트'</li><li>'여성 잠수복 원피스 스노쿨링 서핑 다이빙 슈트 D 스포츠/레저>스킨스쿠버>다이빙슈트'</li><li>'K2 플라이슈트 FLYSUIT THERMO W ECO KWU23164Z1680253 스포츠/레저>스킨스쿠버>다이빙슈트'</li></ul> \|
	\| 10.0 \| <ul><li>'Intbost 스노클링 수영 스쿠버 다이빙 양말 네오프렌 비치 사커 배구용 잠수복 양말 스포츠/레저>스킨스쿠버>부츠'</li><li>'다이빙신발 부츠 다이빙슈즈 아쿠아슈즈 신발 스쿠버-E 블랙사이즈 40 남성용 스포츠/레저>스킨스쿠버>부츠'</li><li>'더블케이 양면 발목삭스 프리다이빙 스노쿨링 스쿠버 스포츠/레저>스킨스쿠버>부츠'</li></ul> \|
	\| 1.0 \| <ul><li>'리프투어러 스노클링 마린 장갑 BK RA-0202 스포츠/레저>스킨스쿠버>글러브'</li><li>'이온 ION 아이비 앞집 여성 보호 조끼 Fins 장갑 Socks and boots 수트 Changing robes 서핑 수건 가드 L Capsule 스포츠/레저>스킨스쿠버>글러브'</li><li>'스쿠버장갑 그립 글러브 강력하고 유연한 구조 미끄럼방지코팅 가볍고편안함 바다 스포츠/레저>스킨스쿠버>글러브'</li></ul> \|
	\| 11.0 \| <ul><li>'다이버용 배터리 1066A 스포츠/레저>스킨스쿠버>수중전등'</li><li>'Wurkkos DL06 다이빙라이트 스킨스쿠버 손전등 15 000루멘 스포츠/레저>스킨스쿠버>수중전등'</li><li>'고화질 수중카메라 피싱캠 낚시용 휴대용 야간 수중캠 스포츠/레저>스킨스쿠버>수중전등'</li></ul> \|
	\| 6.0 \| <ul><li>'스킨스쿠버 수중 해루질 나이프 정글 다이빙05 MO3004 스포츠/레저>스킨스쿠버>다이빙칼'</li><li>'펜싱 장비 가변운 훈련 플라스틱 칼 꽃 경험 안전 히트 스포츠/레저>스킨스쿠버>다이빙칼'</li><li>'수중 스쿠버 해루질 캠핑 울브스 다이버스03 그린 IGWC0741B 스포츠/레저>스킨스쿠버>다이빙칼'</li></ul> \|
	\| 8.0 \| <ul><li>'골프버디 리트렉터 기기미포함 스포츠/레저>스킨스쿠버>리트렉터'</li><li>'인터오션 스킨스쿠버 악세사리 리트렉터 고리형 카메라 고프로 게이지 장비걸이 슈퍼포스 미니 리트렉터2 스포츠/레저>스킨스쿠버>리트렉터'</li><li>'이노베이티브 리트렉터 락 스포츠/레저>스킨스쿠버>리트렉터'</li></ul> \|
	\| 12.0 \| <ul><li>'수중작살 낚시 삼지창 샤크건 바다 해루질 물고기 스포츠/레저>스킨스쿠버>수중총/작살'</li><li>'해루질 스텐봉2p 조과통 작살뜰채 해루질뜰채 8인치 이동식 품 해루질용 해루질수경 스포츠/레저>스킨스쿠버>수중총/작살'</li><li>'해루질 스틸 스테인레스 물고기 작살촉 포크 장비 스포츠/레저>스킨스쿠버>수중총/작살'</li></ul> \|
	\| 2.0 \| <ul><li>'프리 다이빙 백팩 롱핀 핀 가방 팩 스쿠버 장비 30L MK031900330LW1 스포츠/레저>스킨스쿠버>기어백'</li><li>'스쿠버다이빙가방 롱핀 하드케이스 프리다이빙 핀백 -길이 너비 스포츠/레저>스킨스쿠버>기어백'</li><li>'프리다이빙 롱핀가방 하드케이스 스쿠버 장비박스 백팩 스포츠/레저>스킨스쿠버>기어백'</li></ul> \|
	\| 13.0 \| <ul><li>'마레스 마니체타 롱 호스 210cm 3/8인치 - MARES 스쿠버 다이빙 스포츠/레저 > 스킨스쿠버 > 호스'</li><li>'마레스 롱 호스 2M 주호스용 MARES 스쿠버 다이빙 스포츠/레저 > 스킨스쿠버 > 호스'</li><li>'스킨스쿠버 호스 헬멧 다이빙 MZ00 3 in 스포츠/레저 > 스킨스쿠버 > 호스'</li></ul> \|
	\| 9.0 \| <ul><li>'Cressi Starter Pro 패키지 스포츠/레저>스킨스쿠버>부력재킷'</li><li>'Deep See by Aqua Lung 컴팩트 리트랙터 키트 BCD용 스포츠/레저>스킨스쿠버>부력재킷'</li><li>'마레스 부력조절기 Mares SCUBA Magellan BCD Size 스포츠/레저>스킨스쿠버>부력재킷'</li></ul> \|
	\| 4.0 \| <ul><li>'원브레스 코팅납 2kg 스포츠/레저>스킨스쿠버>납'</li><li>'랍스터 해비 넥 웨이트 500g 스포츠/레저>스킨스쿠버>납'</li><li>"MOBBY'S 발목웨이트 ACG-9000 500gx2개 스포츠/레저>스킨스쿠버>납"</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_sl20")
	# Run inference
	preds = model("브로브 수영랜턴 고급형 스노쿨링 잠수후레쉬 CREE 스포츠/레저>스킨스쿠버>수중전등")
	```

	<!--
	### Downstream Use

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

	<!--
	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
	-->

	<!--
	## 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.
	-->

	<!--
	### 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 \| 2 \| 9.2506 \| 24 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0.0 \| 69 \|
	\| 1.0 \| 70 \|
	\| 2.0 \| 70 \|
	\| 3.0 \| 70 \|
	\| 4.0 \| 9 \|
	\| 5.0 \| 70 \|
	\| 6.0 \| 70 \|
	\| 7.0 \| 70 \|
	\| 8.0 \| 70 \|
	\| 9.0 \| 70 \|
	\| 10.0 \| 70 \|
	\| 11.0 \| 70 \|
	\| 12.0 \| 70 \|
	\| 13.0 \| 10 \|

	### 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0060 \| 1 \| 0.5025 \| - \|
	\| 0.2976 \| 50 \| 0.4963 \| - \|
	\| 0.5952 \| 100 \| 0.3183 \| - \|
	\| 0.8929 \| 150 \| 0.0275 \| - \|
	\| 1.1905 \| 200 \| 0.0142 \| - \|
	\| 1.4881 \| 250 \| 0.0142 \| - \|
	\| 1.7857 \| 300 \| 0.0132 \| - \|
	\| 2.0833 \| 350 \| 0.0144 \| - \|
	\| 2.3810 \| 400 \| 0.0097 \| - \|
	\| 2.6786 \| 450 \| 0.001 \| - \|
	\| 2.9762 \| 500 \| 0.0002 \| - \|
	\| 3.2738 \| 550 \| 0.0 \| - \|
	\| 3.5714 \| 600 \| 0.0 \| - \|
	\| 3.8690 \| 650 \| 0.0 \| - \|
	\| 4.1667 \| 700 \| 0.0 \| - \|
	\| 4.4643 \| 750 \| 0.0 \| - \|
	\| 4.7619 \| 800 \| 0.0001 \| - \|
	\| 5.0595 \| 850 \| 0.0 \| - \|
	\| 5.3571 \| 900 \| 0.0 \| - \|
	\| 5.6548 \| 950 \| 0.0 \| - \|
	\| 5.9524 \| 1000 \| 0.0 \| - \|
	\| 6.25 \| 1050 \| 0.0 \| - \|
	\| 6.5476 \| 1100 \| 0.0 \| - \|
	\| 6.8452 \| 1150 \| 0.0 \| - \|
	\| 7.1429 \| 1200 \| 0.0 \| - \|
	\| 7.4405 \| 1250 \| 0.0 \| - \|
	\| 7.7381 \| 1300 \| 0.0 \| - \|
	\| 8.0357 \| 1350 \| 0.0 \| - \|
	\| 8.3333 \| 1400 \| 0.0 \| - \|
	\| 8.6310 \| 1450 \| 0.0 \| - \|
	\| 8.9286 \| 1500 \| 0.0 \| - \|
	\| 9.2262 \| 1550 \| 0.0 \| - \|
	\| 9.5238 \| 1600 \| 0.0 \| - \|
	\| 9.8214 \| 1650 \| 0.0 \| - \|
	\| 10.1190 \| 1700 \| 0.0 \| - \|
	\| 10.4167 \| 1750 \| 0.0 \| - \|
	\| 10.7143 \| 1800 \| 0.0 \| - \|
	\| 11.0119 \| 1850 \| 0.0 \| - \|
	\| 11.3095 \| 1900 \| 0.0 \| - \|
	\| 11.6071 \| 1950 \| 0.0 \| - \|
	\| 11.9048 \| 2000 \| 0.0 \| - \|
	\| 12.2024 \| 2050 \| 0.0 \| - \|
	\| 12.5 \| 2100 \| 0.0 \| - \|
	\| 12.7976 \| 2150 \| 0.0006 \| - \|
	\| 13.0952 \| 2200 \| 0.0001 \| - \|
	\| 13.3929 \| 2250 \| 0.0 \| - \|
	\| 13.6905 \| 2300 \| 0.0 \| - \|
	\| 13.9881 \| 2350 \| 0.0 \| - \|
	\| 14.2857 \| 2400 \| 0.0 \| - \|
	\| 14.5833 \| 2450 \| 0.0 \| - \|
	\| 14.8810 \| 2500 \| 0.0 \| - \|
	\| 15.1786 \| 2550 \| 0.0 \| - \|
	\| 15.4762 \| 2600 \| 0.0 \| - \|
	\| 15.7738 \| 2650 \| 0.0 \| - \|
	\| 16.0714 \| 2700 \| 0.0 \| - \|
	\| 16.3690 \| 2750 \| 0.0 \| - \|
	\| 16.6667 \| 2800 \| 0.0 \| - \|
	\| 16.9643 \| 2850 \| 0.0 \| - \|
	\| 17.2619 \| 2900 \| 0.0 \| - \|
	\| 17.5595 \| 2950 \| 0.0 \| - \|
	\| 17.8571 \| 3000 \| 0.0 \| - \|
	\| 18.1548 \| 3050 \| 0.0 \| - \|
	\| 18.4524 \| 3100 \| 0.0 \| - \|
	\| 18.75 \| 3150 \| 0.0 \| - \|
	\| 19.0476 \| 3200 \| 0.0 \| - \|
	\| 19.3452 \| 3250 \| 0.0 \| - \|
	\| 19.6429 \| 3300 \| 0.0 \| - \|
	\| 19.9405 \| 3350 \| 0.0 \| - \|
	\| 20.2381 \| 3400 \| 0.0 \| - \|
	\| 20.5357 \| 3450 \| 0.0 \| - \|
	\| 20.8333 \| 3500 \| 0.0 \| - \|
	\| 21.1310 \| 3550 \| 0.0 \| - \|
	\| 21.4286 \| 3600 \| 0.0 \| - \|
	\| 21.7262 \| 3650 \| 0.0 \| - \|
	\| 22.0238 \| 3700 \| 0.0 \| - \|
	\| 22.3214 \| 3750 \| 0.0 \| - \|
	\| 22.6190 \| 3800 \| 0.0 \| - \|
	\| 22.9167 \| 3850 \| 0.0 \| - \|
	\| 23.2143 \| 3900 \| 0.0 \| - \|
	\| 23.5119 \| 3950 \| 0.0002 \| - \|
	\| 23.8095 \| 4000 \| 0.0 \| - \|
	\| 24.1071 \| 4050 \| 0.0 \| - \|
	\| 24.4048 \| 4100 \| 0.0 \| - \|
	\| 24.7024 \| 4150 \| 0.0 \| - \|
	\| 25.0 \| 4200 \| 0.0 \| - \|
	\| 25.2976 \| 4250 \| 0.0 \| - \|
	\| 25.5952 \| 4300 \| 0.0 \| - \|
	\| 25.8929 \| 4350 \| 0.0 \| - \|
	\| 26.1905 \| 4400 \| 0.0 \| - \|
	\| 26.4881 \| 4450 \| 0.0 \| - \|
	\| 26.7857 \| 4500 \| 0.0 \| - \|
	\| 27.0833 \| 4550 \| 0.0 \| - \|
	\| 27.3810 \| 4600 \| 0.0 \| - \|
	\| 27.6786 \| 4650 \| 0.0 \| - \|
	\| 27.9762 \| 4700 \| 0.0 \| - \|
	\| 28.2738 \| 4750 \| 0.0 \| - \|
	\| 28.5714 \| 4800 \| 0.0 \| - \|
	\| 28.8690 \| 4850 \| 0.0 \| - \|
	\| 29.1667 \| 4900 \| 0.0 \| - \|
	\| 29.4643 \| 4950 \| 0.0 \| - \|
	\| 29.7619 \| 5000 \| 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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