master_cate_sl4 / README.md

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	---
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: 가퍼 스포츠 낚시 벨트 어깨 하 해상 스탠드업 물고기 싸움 로드 홀더 스포츠/레저>낚시>낚시의류/잡화>힙커버/힙가드
	- text: 낚시 태클박스 36리터 세트8 초경량 멀티 테이블 의자 받침대 루어 민물 바다 케리어 BSS158-3 스포츠/레저>낚시>낚시용품>태클박스
	- text: 메이저 크래프트 자이언트 킬링 Major Craft GK5SJ-B663 스포츠/레저>낚시>루어낚시>루어낚시세트
	- text: 갸프 낚싯대 용골 핸들 땀 흡수 스트랩 미끄럼 방지 절연 라켓 손잡이 커버 스포츠/레저>낚시>낚시용품>가프
	- text: 송어베이스 루어 세트 스푼 미끼 스피너 보빈 인공 스포츠/레저>낚시>루어낚시>루어낚시세트
	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: 8 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 \|
	\|:------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 7.0 \| <ul><li>'다이와 DAIWA 한국다이와정공 소품케이스 클리어 파우치 S C 스포츠/레저>낚시>바다낚시>찌케이스'</li><li>'갓포스 고급 루어 낚시가방 루어대 원투대 하드 로드케이스 낚시대수납 단품 112CM-157CM 스포츠/레저>낚시>바다낚시>바다낚시가방'</li><li>'다이와 포터블 휴대용 로드케이스 B 140R 스포츠/레저>낚시>바다낚시>바다낚시가방'</li></ul> \|
	\| 3.0 \| <ul><li>'이공조구 원 포인트 바다루어낚싯대 S180 스포츠/레저>낚시>낚싯대>바다루어낚싯대'</li><li>'엔에스 블랙 매직아이 슬로우피치 바다루어낚싯대 B-592H3MF 스포츠/레저>낚시>낚싯대>바다루어낚싯대'</li><li>'은성 실스타 DHC 명파S 민물낚싯대 30칸 스포츠/레저>낚시>낚싯대>민물낚싯대'</li></ul> \|
	\| 1.0 \| <ul><li>'메이호 태클박스 루어케이스 도구통 지그통 VS-388DD 스포츠/레저>낚시>낚시용품>태클박스'</li><li>'다이와 쿨라인 알파 3 펄 TS2000 스포츠/레저>낚시>낚시용품>쿨백'</li><li>'슬라이드 낚시 쪽가위 라인커터기 합사가위 T74464474 스포츠/레저>낚시>낚시공구>가위/라인커터/핀온릴'</li></ul> \|
	\| 5.0 \| <ul><li>'다미끼 맘바2 러버지그-배스 루어 민물루어 1 2oz 스포츠/레저>낚시>루어낚시>하드베이트'</li><li>'루어 낚시 가물치 배스 5pcs 개구리 세트 프로그 스포츠/레저>낚시>루어낚시>루어낚시세트'</li><li>'KFP 미노우 KS01 하드베이트 싱킹타입 루어 포퍼 웜 크랭크 프로팅 싱킹 배스 미끼 농어 베이트 스포츠/레저>낚시>루어낚시>하드베이트'</li></ul> \|
	\| 0.0 \| <ul><li>'다이와 레브로스 스피닝릴 LT2500D-XH 스포츠/레저>낚시>낚시릴>스피닝릴'</li><li>'바낙스 LJ100x 장구통릴 티탄 스포츠/레저>낚시>낚시릴>베이트릴'</li><li>'시마노 FX 1000 스피닝릴 스포츠/레저>낚시>낚시릴>스피닝릴'</li></ul> \|
	\| 4.0 \| <ul><li>'가마라 쇼크리더 카본 목줄 50m 6호 GFLUORO506 스포츠/레저>낚시>낚싯줄>카본라인'</li><li>'선라인 토네이도 마츠다 스페셜 블랙 스트림 낚싯줄 70m 1.75호 스포츠/레저>낚시>낚싯줄>카본라인'</li><li>'선라인 슈터 FC 스나이퍼 100m 4.5LB 스포츠/레저>낚시>낚싯줄>카본라인'</li></ul> \|
	\| 2.0 \| <ul><li>'다이와 낚시화 부츠 운동화 스파이크 슈즈 DAIWA 일본직구 DS-2150CD 스포츠/레저>낚시>낚시의류/잡화>낚시신발'</li><li>'HDF 해동 피나투라 올컷 방한 덮개장갑 낚시장갑 스포츠/레저>낚시>낚시의류/잡화>낚시장갑'</li><li>'가마가츠 낚시 코듀라 힙가드 로우백 타입 단일사이즈 GM3727 스포츠/레저>낚시>낚시의류/잡화>힙커버/힙가드'</li></ul> \|
	\| 6.0 \| <ul><li>'루웍스 빙어 초릿대 23cm 스포츠/레저>낚시>민물낚시>얼음낚시'</li><li>'바다 민물 고기 낚시대 보관 수납 가방 하드케이스 스포츠/레저>낚시>민물낚시>민물낚시가방'</li><li>'고급 내림찌케이스 대형찌보관함 플로팅 보관박스 스포츠/레저>낚시>민물낚시>찌케이스'</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_sl4")
	# Run inference
	preds = model("송어베이스 루어 세트 스푼 미끼 스피너 보빈 인공 스포츠/레저>낚시>루어낚시>루어낚시세트")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 2 \| 7.8018 \| 19 \|

	\| 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 \|

	### 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0091 \| 1 \| 0.4946 \| - \|
	\| 0.4545 \| 50 \| 0.5017 \| - \|
	\| 0.9091 \| 100 \| 0.2322 \| - \|
	\| 1.3636 \| 150 \| 0.0559 \| - \|
	\| 1.8182 \| 200 \| 0.0182 \| - \|
	\| 2.2727 \| 250 \| 0.0165 \| - \|
	\| 2.7273 \| 300 \| 0.0018 \| - \|
	\| 3.1818 \| 350 \| 0.0001 \| - \|
	\| 3.6364 \| 400 \| 0.0001 \| - \|
	\| 4.0909 \| 450 \| 0.0001 \| - \|
	\| 4.5455 \| 500 \| 0.0 \| - \|
	\| 5.0 \| 550 \| 0.0 \| - \|
	\| 5.4545 \| 600 \| 0.0 \| - \|
	\| 5.9091 \| 650 \| 0.0 \| - \|
	\| 6.3636 \| 700 \| 0.0 \| - \|
	\| 6.8182 \| 750 \| 0.0 \| - \|
	\| 7.2727 \| 800 \| 0.0 \| - \|
	\| 7.7273 \| 850 \| 0.0 \| - \|
	\| 8.1818 \| 900 \| 0.0 \| - \|
	\| 8.6364 \| 950 \| 0.0 \| - \|
	\| 9.0909 \| 1000 \| 0.0 \| - \|
	\| 9.5455 \| 1050 \| 0.0 \| - \|
	\| 10.0 \| 1100 \| 0.0 \| - \|
	\| 10.4545 \| 1150 \| 0.0 \| - \|
	\| 10.9091 \| 1200 \| 0.0 \| - \|
	\| 11.3636 \| 1250 \| 0.0 \| - \|
	\| 11.8182 \| 1300 \| 0.0 \| - \|
	\| 12.2727 \| 1350 \| 0.0 \| - \|
	\| 12.7273 \| 1400 \| 0.0 \| - \|
	\| 13.1818 \| 1450 \| 0.0 \| - \|
	\| 13.6364 \| 1500 \| 0.0 \| - \|
	\| 14.0909 \| 1550 \| 0.0 \| - \|
	\| 14.5455 \| 1600 \| 0.0 \| - \|
	\| 15.0 \| 1650 \| 0.0 \| - \|
	\| 15.4545 \| 1700 \| 0.0 \| - \|
	\| 15.9091 \| 1750 \| 0.0 \| - \|
	\| 16.3636 \| 1800 \| 0.0 \| - \|
	\| 16.8182 \| 1850 \| 0.0 \| - \|
	\| 17.2727 \| 1900 \| 0.0 \| - \|
	\| 17.7273 \| 1950 \| 0.0 \| - \|
	\| 18.1818 \| 2000 \| 0.0 \| - \|
	\| 18.6364 \| 2050 \| 0.0 \| - \|
	\| 19.0909 \| 2100 \| 0.0 \| - \|
	\| 19.5455 \| 2150 \| 0.0 \| - \|
	\| 20.0 \| 2200 \| 0.0 \| - \|
	\| 20.4545 \| 2250 \| 0.0 \| - \|
	\| 20.9091 \| 2300 \| 0.0 \| - \|
	\| 21.3636 \| 2350 \| 0.0 \| - \|
	\| 21.8182 \| 2400 \| 0.0 \| - \|
	\| 22.2727 \| 2450 \| 0.0 \| - \|
	\| 22.7273 \| 2500 \| 0.0 \| - \|
	\| 23.1818 \| 2550 \| 0.0 \| - \|
	\| 23.6364 \| 2600 \| 0.0 \| - \|
	\| 24.0909 \| 2650 \| 0.0 \| - \|
	\| 24.5455 \| 2700 \| 0.0 \| - \|
	\| 25.0 \| 2750 \| 0.0 \| - \|
	\| 25.4545 \| 2800 \| 0.0 \| - \|
	\| 25.9091 \| 2850 \| 0.0 \| - \|
	\| 26.3636 \| 2900 \| 0.0 \| - \|
	\| 26.8182 \| 2950 \| 0.0 \| - \|
	\| 27.2727 \| 3000 \| 0.0 \| - \|
	\| 27.7273 \| 3050 \| 0.0 \| - \|
	\| 28.1818 \| 3100 \| 0.0 \| - \|
	\| 28.6364 \| 3150 \| 0.0 \| - \|
	\| 29.0909 \| 3200 \| 0.0 \| - \|
	\| 29.5455 \| 3250 \| 0.0 \| - \|
	\| 30.0 \| 3300 \| 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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