master_item_ap / README.md

Push model using huggingface_hub.

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	---
	base_model: klue/roberta-base
	library_name: setfit
	metrics:
	- metric
	pipeline_tag: text-classification
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: 뉴발란스패딩 BQC NBNPB41043-16 UNI 액티브 숏 나일론 구스다운 자켓 105 (주)씨제이이엔엠
	- text: 드로우핏X노이어 핸드메이드 캐시미어 싱글 코트 DRAW FIT X NOIRER HANDMADE CASHMERE SINGLE COAT
	550182 M 버베나
	- text: 언더아머 야구 점퍼 1375292-400 S 슈즈스타11
	- text: '[Lucky Brand] 럭키브랜드 23FW 슬림핏 코듀로이 팬츠 1종 크림_55 (주)씨제이이엔엠'
	- text: '[롯데백화점]탱커스 바스락 후드 여름 점퍼 (TV1JP013M0) 블랙_F 롯데백화점_'
	inference: true
	model-index:
	- name: SetFit with klue/roberta-base
	results:
	- task:
	type: text-classification
	name: Text Classification
	dataset:
	name: Unknown
	type: unknown
	split: test
	metrics:
	- type: metric
	value: 0.8999370266909948
	name: Metric
	---

	# SetFit with klue/roberta-base

	This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [klue/roberta-base](https://huggingface.co/klue/roberta-base) 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: [klue/roberta-base](https://huggingface.co/klue/roberta-base)
	- 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: 4 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>'갤러리아 GUESS Jeans S/S [공용] NO1D0023 M톤 슬림 와이드 미디엄블루_28 갤러리아백화점'</li><li>'[현대백화점][헤지스남성] 케이블 울 하프 집업 니트 HZSW3D326G2 [00004] 그레이(G2)/110 (주)현대홈쇼핑'</li><li>'데일리 플랩 항공 점퍼BK BK_110 (주) 패션플러스'</li></ul> \|
	\| 2.0 \| <ul><li>'스파오 산리오캐릭터즈 수면잠옷BLACKSPPPD4TU03 SPPPD4TU03 19 BLACK_L 100 시그마인터내셔널'</li><li>'BYC여성 순면내복내의 베이직여상하2호 BYT6656 베이직여상하_인디안핑크_90 세종유통'</li><li>'BYT3842 BYC 데오니아 심플 순면 여자 끈 나시 런닝 검정색_100 에이치앤비 주식회사'</li></ul> \|
	\| 3.0 \| <ul><li>'[켄지 24SS 최신상] ○ 24SS 오가닉 코튼 100 니트 4종 105 '</li><li>'[갤러리아] 울 아가일 배색 가디건(한화갤러리아㈜ 센터시티) 라이트그레이LG82020_66 한화갤러리아(주)'</li><li>'[오우오](신세계의정부점)벨리SET / W3F91ST03 핑크_FR 주식회사 에스에스지닷컴'</li></ul> \|
	\| 0.0 \| <ul><li>'[현대백화점]엘르이너웨어_ EBMRN713BK 모달에어로웜와플 남런닝BK 95 (주)현대백화점'</li><li>'비너스(정상) 비너스 면 80수 이합 지그재그 나염 남성 런닝 트렁크 세트_A VMV41 블루(BU)/100_필수선택 (주) 패션플러스'</li><li>'JHMRU007 제임스딘 순면 V넥 남성 민소매 머슬 런닝 2_110 도도shop'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Metric \|
	\|:--------\|:-------\|
	\| all \| 0.8999 \|

	## 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_item_ap")
	# Run inference
	preds = model("언더아머 야구 점퍼 1375292-400 S 슈즈스타11")
	```

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	### Downstream Use

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

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	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
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	## 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 \| 3 \| 9.6403 \| 24 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0.0 \| 300 \|
	\| 1.0 \| 809 \|
	\| 2.0 \| 457 \|
	\| 3.0 \| 1050 \|

	### Training Hyperparameters
	- batch_size: (512, 512)
	- num_epochs: (20, 20)
	- max_steps: -1
	- sampling_strategy: oversampling
	- num_iterations: 40
	- body_learning_rate: (2e-05, 2e-05)
	- head_learning_rate: 2e-05
	- loss: CosineSimilarityLoss
	- distance_metric: cosine_distance
	- margin: 0.25
	- end_to_end: False
	- use_amp: False
	- warmup_proportion: 0.1
	- seed: 42
	- eval_max_steps: -1
	- load_best_model_at_end: False

	### Training Results
	\| Epoch \| Step \| Training Loss \| Validation Loss \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0024 \| 1 \| 0.4029 \| - \|
	\| 0.1222 \| 50 \| 0.3584 \| - \|
	\| 0.2445 \| 100 \| 0.2822 \| - \|
	\| 0.3667 \| 150 \| 0.2453 \| - \|
	\| 0.4890 \| 200 \| 0.1961 \| - \|
	\| 0.6112 \| 250 \| 0.1677 \| - \|
	\| 0.7335 \| 300 \| 0.1175 \| - \|
	\| 0.8557 \| 350 \| 0.0615 \| - \|
	\| 0.9780 \| 400 \| 0.0308 \| - \|
	\| 1.1002 \| 450 \| 0.0218 \| - \|
	\| 1.2225 \| 500 \| 0.0133 \| - \|
	\| 1.3447 \| 550 \| 0.0058 \| - \|
	\| 1.4670 \| 600 \| 0.0101 \| - \|
	\| 1.5892 \| 650 \| 0.002 \| - \|
	\| 1.7115 \| 700 \| 0.0022 \| - \|
	\| 1.8337 \| 750 \| 0.0023 \| - \|
	\| 1.9560 \| 800 \| 0.0041 \| - \|
	\| 2.0782 \| 850 \| 0.0057 \| - \|
	\| 2.2005 \| 900 \| 0.0001 \| - \|
	\| 2.3227 \| 950 \| 0.0029 \| - \|
	\| 2.4450 \| 1000 \| 0.0032 \| - \|
	\| 2.5672 \| 1050 \| 0.004 \| - \|
	\| 2.6895 \| 1100 \| 0.0021 \| - \|
	\| 2.8117 \| 1150 \| 0.0033 \| - \|
	\| 2.9340 \| 1200 \| 0.002 \| - \|
	\| 3.0562 \| 1250 \| 0.002 \| - \|
	\| 3.1785 \| 1300 \| 0.0019 \| - \|
	\| 3.3007 \| 1350 \| 0.0 \| - \|
	\| 3.4230 \| 1400 \| 0.0019 \| - \|
	\| 3.5452 \| 1450 \| 0.0 \| - \|
	\| 3.6675 \| 1500 \| 0.0039 \| - \|
	\| 3.7897 \| 1550 \| 0.0 \| - \|
	\| 3.9120 \| 1600 \| 0.0 \| - \|
	\| 4.0342 \| 1650 \| 0.0002 \| - \|
	\| 4.1565 \| 1700 \| 0.0049 \| - \|
	\| 4.2787 \| 1750 \| 0.002 \| - \|
	\| 4.4010 \| 1800 \| 0.0 \| - \|
	\| 4.5232 \| 1850 \| 0.0026 \| - \|
	\| 4.6455 \| 1900 \| 0.0 \| - \|
	\| 4.7677 \| 1950 \| 0.0 \| - \|
	\| 4.8900 \| 2000 \| 0.0001 \| - \|
	\| 5.0122 \| 2050 \| 0.002 \| - \|
	\| 5.1345 \| 2100 \| 0.002 \| - \|
	\| 5.2567 \| 2150 \| 0.0 \| - \|
	\| 5.3790 \| 2200 \| 0.0 \| - \|
	\| 5.5012 \| 2250 \| 0.0 \| - \|
	\| 5.6235 \| 2300 \| 0.0 \| - \|
	\| 5.7457 \| 2350 \| 0.0004 \| - \|
	\| 5.8680 \| 2400 \| 0.0019 \| - \|
	\| 5.9902 \| 2450 \| 0.0018 \| - \|
	\| 6.1125 \| 2500 \| 0.0 \| - \|
	\| 6.2347 \| 2550 \| 0.0 \| - \|
	\| 6.3570 \| 2600 \| 0.0 \| - \|
	\| 6.4792 \| 2650 \| 0.0 \| - \|
	\| 6.6015 \| 2700 \| 0.002 \| - \|
	\| 6.7237 \| 2750 \| 0.0009 \| - \|
	\| 6.8460 \| 2800 \| 0.0 \| - \|
	\| 6.9682 \| 2850 \| 0.0015 \| - \|
	\| 7.0905 \| 2900 \| 0.0001 \| - \|
	\| 7.2127 \| 2950 \| 0.0001 \| - \|
	\| 7.3350 \| 3000 \| 0.002 \| - \|
	\| 7.4572 \| 3050 \| 0.0001 \| - \|
	\| 7.5795 \| 3100 \| 0.0001 \| - \|
	\| 7.7017 \| 3150 \| 0.0019 \| - \|
	\| 7.8240 \| 3200 \| 0.0019 \| - \|
	\| 7.9462 \| 3250 \| 0.0 \| - \|
	\| 8.0685 \| 3300 \| 0.0001 \| - \|
	\| 8.1907 \| 3350 \| 0.0038 \| - \|
	\| 8.3130 \| 3400 \| 0.0 \| - \|
	\| 8.4352 \| 3450 \| 0.0018 \| - \|
	\| 8.5575 \| 3500 \| 0.0 \| - \|
	\| 8.6797 \| 3550 \| 0.0019 \| - \|
	\| 8.8020 \| 3600 \| 0.0 \| - \|
	\| 8.9242 \| 3650 \| 0.0 \| - \|
	\| 9.0465 \| 3700 \| 0.0 \| - \|
	\| 9.1687 \| 3750 \| 0.0 \| - \|
	\| 9.2910 \| 3800 \| 0.0 \| - \|
	\| 9.4132 \| 3850 \| 0.0001 \| - \|
	\| 9.5355 \| 3900 \| 0.0 \| - \|
	\| 9.6577 \| 3950 \| 0.0019 \| - \|
	\| 9.7800 \| 4000 \| 0.0019 \| - \|
	\| 9.9022 \| 4050 \| 0.0 \| - \|
	\| 10.0244 \| 4100 \| 0.0001 \| - \|
	\| 10.1467 \| 4150 \| 0.0 \| - \|
	\| 10.2689 \| 4200 \| 0.002 \| - \|
	\| 10.3912 \| 4250 \| 0.0 \| - \|
	\| 10.5134 \| 4300 \| 0.0 \| - \|
	\| 10.6357 \| 4350 \| 0.0 \| - \|
	\| 10.7579 \| 4400 \| 0.0 \| - \|
	\| 10.8802 \| 4450 \| 0.0 \| - \|
	\| 11.0024 \| 4500 \| 0.0 \| - \|
	\| 11.1247 \| 4550 \| 0.0018 \| - \|
	\| 11.2469 \| 4600 \| 0.0 \| - \|
	\| 11.3692 \| 4650 \| 0.0 \| - \|
	\| 11.4914 \| 4700 \| 0.0 \| - \|
	\| 11.6137 \| 4750 \| 0.0 \| - \|
	\| 11.7359 \| 4800 \| 0.0019 \| - \|
	\| 11.8582 \| 4850 \| 0.001 \| - \|
	\| 11.9804 \| 4900 \| 0.0 \| - \|
	\| 12.1027 \| 4950 \| 0.0001 \| - \|
	\| 12.2249 \| 5000 \| 0.0 \| - \|
	\| 12.3472 \| 5050 \| 0.0 \| - \|
	\| 12.4694 \| 5100 \| 0.0 \| - \|
	\| 12.5917 \| 5150 \| 0.0 \| - \|
	\| 12.7139 \| 5200 \| 0.0 \| - \|
	\| 12.8362 \| 5250 \| 0.0 \| - \|
	\| 12.9584 \| 5300 \| 0.0 \| - \|
	\| 13.0807 \| 5350 \| 0.0001 \| - \|
	\| 13.2029 \| 5400 \| 0.0001 \| - \|
	\| 13.3252 \| 5450 \| 0.0 \| - \|
	\| 13.4474 \| 5500 \| 0.0001 \| - \|
	\| 13.5697 \| 5550 \| 0.0 \| - \|
	\| 13.6919 \| 5600 \| 0.0 \| - \|
	\| 13.8142 \| 5650 \| 0.0 \| - \|
	\| 13.9364 \| 5700 \| 0.0 \| - \|
	\| 14.0587 \| 5750 \| 0.0001 \| - \|
	\| 14.1809 \| 5800 \| 0.0 \| - \|
	\| 14.3032 \| 5850 \| 0.0 \| - \|
	\| 14.4254 \| 5900 \| 0.0 \| - \|
	\| 14.5477 \| 5950 \| 0.0 \| - \|
	\| 14.6699 \| 6000 \| 0.0 \| - \|
	\| 14.7922 \| 6050 \| 0.0 \| - \|
	\| 14.9144 \| 6100 \| 0.0 \| - \|
	\| 15.0367 \| 6150 \| 0.0 \| - \|
	\| 15.1589 \| 6200 \| 0.0 \| - \|
	\| 15.2812 \| 6250 \| 0.0 \| - \|
	\| 15.4034 \| 6300 \| 0.0 \| - \|
	\| 15.5257 \| 6350 \| 0.0 \| - \|
	\| 15.6479 \| 6400 \| 0.0 \| - \|
	\| 15.7702 \| 6450 \| 0.0 \| - \|
	\| 15.8924 \| 6500 \| 0.0 \| - \|
	\| 16.0147 \| 6550 \| 0.0 \| - \|
	\| 16.1369 \| 6600 \| 0.0 \| - \|
	\| 16.2592 \| 6650 \| 0.0 \| - \|
	\| 16.3814 \| 6700 \| 0.0 \| - \|
	\| 16.5037 \| 6750 \| 0.0 \| - \|
	\| 16.6259 \| 6800 \| 0.0 \| - \|
	\| 16.7482 \| 6850 \| 0.0 \| - \|
	\| 16.8704 \| 6900 \| 0.0 \| - \|
	\| 16.9927 \| 6950 \| 0.0 \| - \|
	\| 17.1149 \| 7000 \| 0.0 \| - \|
	\| 17.2372 \| 7050 \| 0.0 \| - \|
	\| 17.3594 \| 7100 \| 0.0 \| - \|
	\| 17.4817 \| 7150 \| 0.0 \| - \|
	\| 17.6039 \| 7200 \| 0.0 \| - \|
	\| 17.7262 \| 7250 \| 0.0 \| - \|
	\| 17.8484 \| 7300 \| 0.0 \| - \|
	\| 17.9707 \| 7350 \| 0.0 \| - \|
	\| 18.0929 \| 7400 \| 0.0 \| - \|
	\| 18.2152 \| 7450 \| 0.0 \| - \|
	\| 18.3374 \| 7500 \| 0.0 \| - \|
	\| 18.4597 \| 7550 \| 0.0 \| - \|
	\| 18.5819 \| 7600 \| 0.0 \| - \|
	\| 18.7042 \| 7650 \| 0.0 \| - \|
	\| 18.8264 \| 7700 \| 0.0 \| - \|
	\| 18.9487 \| 7750 \| 0.0 \| - \|
	\| 19.0709 \| 7800 \| 0.0 \| - \|
	\| 19.1932 \| 7850 \| 0.0 \| - \|
	\| 19.3154 \| 7900 \| 0.0 \| - \|
	\| 19.4377 \| 7950 \| 0.0 \| - \|
	\| 19.5599 \| 8000 \| 0.0 \| - \|
	\| 19.6822 \| 8050 \| 0.0 \| - \|
	\| 19.8044 \| 8100 \| 0.0 \| - \|
	\| 19.9267 \| 8150 \| 0.0 \| - \|

	### Framework Versions
	- Python: 3.10.12
	- SetFit: 1.1.0.dev0
	- Sentence Transformers: 3.1.1
	- Transformers: 4.46.1
	- PyTorch: 2.4.0+cu121
	- Datasets: 2.20.0
	- Tokenizers: 0.20.0

	## 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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