master_cate_sl22 / README.md

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	---
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: 생활원사 수비용 야구장갑 스포츠/레저>야구>야구장갑
	- text: 야구 타석 매트 피칭 발판 잔디 맞춤 마운드 스포츠/레저>야구>기타야구용품
	- text: 수구 헬멧 수구모 귀 보호 모자 훈련 대회 수중 하키 특수 경기 스포츠/레저>야구>헬멧
	- text: MLB 2023 샌디에이고 파드리스 홈 어웨이 김하성 마킹 외 선수 유니폼 져지 스포츠/레저>야구>야구의류
	- text: 롤링스 마하 포수 헬멧 야구 시니어 7 1 8인치 3 4인치 다크 그린 스포츠/레저>야구>포수장비
	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: 12 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>'야구 배팅 연습기 족구 휴대용 실내 시트 거치대 티대 스포츠/레저>야구>기타야구용품'</li><li>'야구 스윙 스틱 장비 스틱 방망이 타격 코칭 도구 스포츠/레저>야구>기타야구용품'</li><li>'Codree 1 Set Baseball Swing Trainer Bands- Elastic Baseball Softball Hitting Resistance Bands- Baseb 스포츠/레저>야구>기타야구용품'</li></ul> \|
	\| 0.0 \| <ul><li>'롤링스 MLB 어센틱 리미티드 HOH 내야 야구글러브 놀란 아레나도 모델 GH2PRONA-BS 스포츠/레저>야구>글러브'</li><li>'윌슨 내야 글러브 11 5 A2000 1786 WBW100390115 스포츠/레저>야구>글러브'</li><li>'모리모토 엣지 올라운드 야구장 우투 글러브 MTG01 스포츠/레저>야구>글러브'</li></ul> \|
	\| 7.0 \| <ul><li>'야구 유니폼 키즈 뉴욕 양키스 레플리카 반팔 저지 스포츠/레저>야구>야구의류'</li><li>'미즈노 오버핏반팔티셔츠 오버핏슬리브 MIZUNO CP SHORT SLEEVE32YA315209 223186 스포츠/레저>야구>야구의류'</li><li>'오타니셔츠 남성 야구 티셔츠 유니폼 메이저리그 TShirt 야구상의 후드 반소매 스포츠/레저>야구>야구의류'</li></ul> \|
	\| 4.0 \| <ul><li>'스타스포츠 야구배트 알루미늄 루키 29in WR310N-0029 스포츠/레저>야구>야구배트'</li><li>'MAJOR 8차 예약 1 메이저 MAZOR 이그나이트 사회인야구 미들탑 골드에디션 알로이배트 구형명기 합금 33 28 32 27 21KBN CU-31 스포츠/레저>야구>야구배트'</li><li>'js야구배트 32 나무배트 알루미늄배트 연식용 야구 스포츠/레저>야구>야구배트'</li></ul> \|
	\| 3.0 \| <ul><li>'롤링스 MLB 로고볼 피츠버그 파이어리츠 스포츠/레저>야구>야구공'</li><li>'MLB 프랭클린 뉴욕양키즈 안전 야구공 2710F10 스포츠/레저>야구>야구공'</li><li>'투더문 안전 야구공 TTMYC-01 스포츠/레저>야구>야구공'</li></ul> \|
	\| 8.0 \| <ul><li>'BBK 24년형 화양연화 Newest 수비장갑 야수용 포수용 스포츠/레저>야구>야구장갑'</li><li>'스타 야구글러브 파워플레이 오른손착용 WG-4100L5-R 스포츠/레저>야구>야구장갑'</li><li>'미즈노 야구 프로 배팅장갑 스카이 1EJEA52221SY D2309 스포츠/레저>야구>야구장갑'</li></ul> \|
	\| 6.0 \| <ul><li>'DESCENTE BASEBALL ROY0 야구 양말 SN111WSO20 C-8710 스포츠/레저>야구>야구양말'</li><li>'미즈노 야구 양말 성인 유소년 12YX1U21 스포츠/레저>야구>야구양말'</li><li>'니드 스포츠 야구양말 스포츠/레저>야구>야구양말'</li></ul> \|
	\| 10.0 \| <ul><li>'Bauer IMS 5 0 하키 헬멧 콤보 안면 마스크 시니어 스포츠/레저>야구>포수장비'</li><li>'아이스 하키 럭비 야구 포수 골키퍼 보호대 모자 헬멧 스포츠/레저>야구>포수장비'</li><li>'포수 마스크 성인용 야구 수비용 올스타 S7 헬멧 어른 그래파이트 투톤 잡기 스포츠/레저>야구>포수장비'</li></ul> \|
	\| 5.0 \| <ul><li>'Markwort 타자를 위한 C-플랩 페이스 프로텍터 스포츠/레저>야구>야구보호대'</li><li>'손가락가드 포수 보호대 야구 엄지보호대 손가락 고정 스포츠/레저>야구>야구보호대'</li><li>'미식축구 Schutt DNA ROPO DW 대표팀 안면 보호대 바시티 스포츠/레저>야구>야구보호대'</li></ul> \|
	\| 2.0 \| <ul><li>'321879 당점 만 즉납 Mizuno 미즈노 미니 케이스 33JM2020 스포츠/레저>야구>야구가방'</li><li>'미즈노 야구 배트 가방 엘리트 1개입 야구배트 수납 케이스 주니어 스포츠/레저>야구>야구가방'</li><li>'드마리니 VOODOO OG 백팩 WB5711707 스포츠/레저>야구>야구가방'</li></ul> \|
	\| 9.0 \| <ul><li>'미즈노 야구화 라이트 레보 프로 GS TPU 11GP230001 스포츠/레저>야구>야구화'</li><li>'언더아머 야구화 포인트화 청 3025590400RO D2309 스포츠/레저>야구>야구화'</li><li>'미즈노 야구 스파이크 쇠붙이 흰색 라이트 레보제로 11GM221501 스포츠/레저>야구>야구화'</li></ul> \|
	\| 11.0 \| <ul><li>'브렛 타자헬멧 양귀 유광 프로 야구헬멧 스포츠/레저>야구>헬멧'</li><li>'모리모토 프로텍티브 2 0 경식 타자 외귀헬멧 유광 블랙레드 스포츠/레저>야구>헬멧'</li><li>'골드 타자헬멧 유광 청색 우귀 좌타자용 검투사 S 유광 청색 우귀 1901-015 스포츠/레저>야구>헬멧'</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_sl22")
	# Run inference
	preds = model("생활원사 수비용 야구장갑 스포츠/레저>야구>야구장갑")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 2 \| 8.3993 \| 20 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0.0 \| 70 \|
	\| 1.0 \| 70 \|
	\| 2.0 \| 69 \|
	\| 3.0 \| 70 \|
	\| 4.0 \| 70 \|
	\| 5.0 \| 70 \|
	\| 6.0 \| 70 \|
	\| 7.0 \| 70 \|
	\| 8.0 \| 70 \|
	\| 9.0 \| 70 \|
	\| 10.0 \| 70 \|
	\| 11.0 \| 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0061 \| 1 \| 0.4871 \| - \|
	\| 0.3049 \| 50 \| 0.4978 \| - \|
	\| 0.6098 \| 100 \| 0.4027 \| - \|
	\| 0.9146 \| 150 \| 0.1531 \| - \|
	\| 1.2195 \| 200 \| 0.0774 \| - \|
	\| 1.5244 \| 250 \| 0.0346 \| - \|
	\| 1.8293 \| 300 \| 0.0227 \| - \|
	\| 2.1341 \| 350 \| 0.0138 \| - \|
	\| 2.4390 \| 400 \| 0.0031 \| - \|
	\| 2.7439 \| 450 \| 0.0004 \| - \|
	\| 3.0488 \| 500 \| 0.0003 \| - \|
	\| 3.3537 \| 550 \| 0.0002 \| - \|
	\| 3.6585 \| 600 \| 0.0002 \| - \|
	\| 3.9634 \| 650 \| 0.0001 \| - \|
	\| 4.2683 \| 700 \| 0.0001 \| - \|
	\| 4.5732 \| 750 \| 0.0001 \| - \|
	\| 4.8780 \| 800 \| 0.0001 \| - \|
	\| 5.1829 \| 850 \| 0.0001 \| - \|
	\| 5.4878 \| 900 \| 0.0001 \| - \|
	\| 5.7927 \| 950 \| 0.0001 \| - \|
	\| 6.0976 \| 1000 \| 0.0001 \| - \|
	\| 6.4024 \| 1050 \| 0.0001 \| - \|
	\| 6.7073 \| 1100 \| 0.0001 \| - \|
	\| 7.0122 \| 1150 \| 0.0001 \| - \|
	\| 7.3171 \| 1200 \| 0.0001 \| - \|
	\| 7.6220 \| 1250 \| 0.0001 \| - \|
	\| 7.9268 \| 1300 \| 0.0 \| - \|
	\| 8.2317 \| 1350 \| 0.0 \| - \|
	\| 8.5366 \| 1400 \| 0.0 \| - \|
	\| 8.8415 \| 1450 \| 0.0 \| - \|
	\| 9.1463 \| 1500 \| 0.0 \| - \|
	\| 9.4512 \| 1550 \| 0.0 \| - \|
	\| 9.7561 \| 1600 \| 0.0 \| - \|
	\| 10.0610 \| 1650 \| 0.0 \| - \|
	\| 10.3659 \| 1700 \| 0.0 \| - \|
	\| 10.6707 \| 1750 \| 0.0 \| - \|
	\| 10.9756 \| 1800 \| 0.0 \| - \|
	\| 11.2805 \| 1850 \| 0.0 \| - \|
	\| 11.5854 \| 1900 \| 0.0 \| - \|
	\| 11.8902 \| 1950 \| 0.0 \| - \|
	\| 12.1951 \| 2000 \| 0.0 \| - \|
	\| 12.5 \| 2050 \| 0.0 \| - \|
	\| 12.8049 \| 2100 \| 0.0001 \| - \|
	\| 13.1098 \| 2150 \| 0.0001 \| - \|
	\| 13.4146 \| 2200 \| 0.0 \| - \|
	\| 13.7195 \| 2250 \| 0.0 \| - \|
	\| 14.0244 \| 2300 \| 0.0 \| - \|
	\| 14.3293 \| 2350 \| 0.0 \| - \|
	\| 14.6341 \| 2400 \| 0.0 \| - \|
	\| 14.9390 \| 2450 \| 0.0 \| - \|
	\| 15.2439 \| 2500 \| 0.0 \| - \|
	\| 15.5488 \| 2550 \| 0.0 \| - \|
	\| 15.8537 \| 2600 \| 0.0 \| - \|
	\| 16.1585 \| 2650 \| 0.0 \| - \|
	\| 16.4634 \| 2700 \| 0.0 \| - \|
	\| 16.7683 \| 2750 \| 0.0 \| - \|
	\| 17.0732 \| 2800 \| 0.0 \| - \|
	\| 17.3780 \| 2850 \| 0.0 \| - \|
	\| 17.6829 \| 2900 \| 0.0 \| - \|
	\| 17.9878 \| 2950 \| 0.0 \| - \|
	\| 18.2927 \| 3000 \| 0.0 \| - \|
	\| 18.5976 \| 3050 \| 0.0 \| - \|
	\| 18.9024 \| 3100 \| 0.0 \| - \|
	\| 19.2073 \| 3150 \| 0.0001 \| - \|
	\| 19.5122 \| 3200 \| 0.0 \| - \|
	\| 19.8171 \| 3250 \| 0.0 \| - \|
	\| 20.1220 \| 3300 \| 0.0 \| - \|
	\| 20.4268 \| 3350 \| 0.0 \| - \|
	\| 20.7317 \| 3400 \| 0.0 \| - \|
	\| 21.0366 \| 3450 \| 0.0 \| - \|
	\| 21.3415 \| 3500 \| 0.0 \| - \|
	\| 21.6463 \| 3550 \| 0.0 \| - \|
	\| 21.9512 \| 3600 \| 0.0 \| - \|
	\| 22.2561 \| 3650 \| 0.0 \| - \|
	\| 22.5610 \| 3700 \| 0.0 \| - \|
	\| 22.8659 \| 3750 \| 0.0 \| - \|
	\| 23.1707 \| 3800 \| 0.0 \| - \|
	\| 23.4756 \| 3850 \| 0.0 \| - \|
	\| 23.7805 \| 3900 \| 0.0 \| - \|
	\| 24.0854 \| 3950 \| 0.0 \| - \|
	\| 24.3902 \| 4000 \| 0.0 \| - \|
	\| 24.6951 \| 4050 \| 0.0 \| - \|
	\| 25.0 \| 4100 \| 0.0 \| - \|
	\| 25.3049 \| 4150 \| 0.0 \| - \|
	\| 25.6098 \| 4200 \| 0.0 \| - \|
	\| 25.9146 \| 4250 \| 0.0 \| - \|
	\| 26.2195 \| 4300 \| 0.0 \| - \|
	\| 26.5244 \| 4350 \| 0.0 \| - \|
	\| 26.8293 \| 4400 \| 0.0 \| - \|
	\| 27.1341 \| 4450 \| 0.0 \| - \|
	\| 27.4390 \| 4500 \| 0.0 \| - \|
	\| 27.7439 \| 4550 \| 0.0 \| - \|
	\| 28.0488 \| 4600 \| 0.0 \| - \|
	\| 28.3537 \| 4650 \| 0.0 \| - \|
	\| 28.6585 \| 4700 \| 0.0 \| - \|
	\| 28.9634 \| 4750 \| 0.0 \| - \|
	\| 29.2683 \| 4800 \| 0.0 \| - \|
	\| 29.5732 \| 4850 \| 0.0 \| - \|
	\| 29.8780 \| 4900 \| 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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