master_cate_fd5 / README.md

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	---
	base_model: mini1013/master_domain
	library_name: setfit
	metrics:
	- metric
	pipeline_tag: text-classification
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: 맑은농산 리얼넛츠 베리앤요거트 하루건강견과 20g x 25개입 비트리
	- text: 23년 햅쌀 골든퀸3호 수향미 특등급 10kg / 순차출고 상상리허설
	- text: 산과들에 원데이오리지널 20g x 50개입 선물세트 동의 제이엠세일즈
	- text: 구운아몬드 1kg 견과류 에이케이에스앤디 (주) AK인터넷쇼핑몰
	- text: 필리핀 세부 건망고 80g 10개-쫀득한망고 말린망고 말린과일 대신유통
	inference: true
	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: metric
	value: 0.894413407821229
	name: Metric
	---

	# 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: 7 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 \|
	\|:------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 6.0 \| <ul><li>'[채울농산] 국산 장수상황버섯(baumii 최상품) 1개월분 (100g) 1개월분 채울농산'</li><li>'명이나물 2kg 산마늘잎 생명이나물 산나물 생채 명이장아찌 강원도 산마늘 명이 장아찌 2kg 토종농장'</li><li>'풀무원 한끼연두부 오리엔탈유자 (118gX2EA) (주)풀무원'</li></ul> \|
	\| 2.0 \| <ul><li>'커클랜드 건 블루베리 567g 몸에 좋은 건과일 샐러드나 베이킹에 활용 코스트코 마인드 트레이드(mind trade)'</li><li>'웰프레쉬 냉동 블루베리 미국산 1kg 배동바지몰'</li><li>'커클랜드 냉동 블루베리 2.27kg 코스트코 아이스박스 요거트 과일 베리 라미의잡화점'</li></ul> \|
	\| 5.0 \| <ul><li>'2022년산 국산 서리태 2kg 검은콩 속청 전남 구례산 볶은 서리태가루 1kg 농업회사법인(주)한결유통'</li><li>'국산 서리태 2kg 검은콩 속청 전남 구례산 국산 서리태(특A) 1kg 농업회사법인(주)한결유통'</li><li>'잔다리마을 특허받은 공법으로 로스팅한 검은콩 서리태 볶음콩 250g / 영양 간식 주식회사 패스트뷰'</li></ul> \|
	\| 0.0 \| <ul><li>'Sol Simple 태양열 건조 망고 6온스(1팩)_파인애플 시이부동'</li><li>'[푸드] KUNNA 쿤나 건망고 75g 3개 부담없이 젤리 망고 마른 과일 태국 간식 사무실 탕비실 건조과일 말린 망고 에스디지컴퍼니'</li><li>'너츠브라더 촉촉한 건망고 200g 건망고 1kg (주)조하'</li></ul> \|
	\| 4.0 \| <ul><li>'[카무트] 고대곡물 카무트 쌀 밀 500g 이푸른(주)'</li><li>'23년 국산 현미 쌀눈 2kg 주식회사 건강중심'</li><li>'[예약구매 할인] 저당 파로 800g 이탈리아 고대곡물 바비조아 저당밥 시리즈 특허공법 저항성전분 주식회사 바비조아'</li></ul> \|
	\| 1.0 \| <ul><li>'맛있는家 너트리 캘리포니아 생아몬드 500g x 2개 (주)씨제이이엔엠'</li><li>'길림양행 탐스팜 쿠키앤크림 아몬드 190g 바이트리스'</li><li>'머거본 커피땅콩 130g 6개/ 견과류 마른안주 주전부리 보마스'</li></ul> \|
	\| 3.0 \| <ul><li>'웰루츠 A등급 냉동 블루베리 1kg 냉동과일 웰루츠 냉동 키위 다이스(중국) 1kg 웰루츠'</li><li>'뉴뜨레 냉동 블루베리 홀 1kg+1kg 무가당 세척블루베리 과일 모음 다이스 퓨레 뉴뜨레 냉동 그린키위 1kg x 2봉 주식회사 보금푸드'</li><li>'코스트코 커클랜드 냉동 블루베리 2.27kg / 아이스박스 포장발송 아이스팩 + 드라이아이스 발송 남들과 다르게'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Metric \|
	\|:--------\|:-------\|
	\| all \| 0.8944 \|

	## 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_fd5")
	# Run inference
	preds = model("구운아몬드 1kg 견과류 에이케이에스앤디 (주) AK인터넷쇼핑몰")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:--------\|:----\|
	\| Word count \| 4 \| 10.0886 \| 25 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0.0 \| 50 \|
	\| 1.0 \| 50 \|
	\| 2.0 \| 50 \|
	\| 3.0 \| 50 \|
	\| 4.0 \| 50 \|
	\| 5.0 \| 50 \|
	\| 6.0 \| 50 \|

	### 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0182 \| 1 \| 0.4119 \| - \|
	\| 0.9091 \| 50 \| 0.2564 \| - \|
	\| 1.8182 \| 100 \| 0.0407 \| - \|
	\| 2.7273 \| 150 \| 0.0157 \| - \|
	\| 3.6364 \| 200 \| 0.014 \| - \|
	\| 4.5455 \| 250 \| 0.0 \| - \|
	\| 5.4545 \| 300 \| 0.0 \| - \|
	\| 6.3636 \| 350 \| 0.0 \| - \|
	\| 7.2727 \| 400 \| 0.0 \| - \|
	\| 8.1818 \| 450 \| 0.0001 \| - \|
	\| 9.0909 \| 500 \| 0.0 \| - \|
	\| 10.0 \| 550 \| 0.0 \| - \|
	\| 10.9091 \| 600 \| 0.0 \| - \|
	\| 11.8182 \| 650 \| 0.0 \| - \|
	\| 12.7273 \| 700 \| 0.0 \| - \|
	\| 13.6364 \| 750 \| 0.0 \| - \|
	\| 14.5455 \| 800 \| 0.0 \| - \|
	\| 15.4545 \| 850 \| 0.0 \| - \|
	\| 16.3636 \| 900 \| 0.0 \| - \|
	\| 17.2727 \| 950 \| 0.0 \| - \|
	\| 18.1818 \| 1000 \| 0.0 \| - \|
	\| 19.0909 \| 1050 \| 0.0 \| - \|
	\| 20.0 \| 1100 \| 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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