master_cate_fd13 / 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: 동원 덴마크 구워먹는 치즈 후라이드 갈릭 125g x 3개 007스테이지스
	- text: 앵커크림치즈 1박스 (1kg x 12개) 앵커크림치즈 1박스 (1kg x 12개) (주)비오비
	- text: 홉라 무가당 휘핑크림 1L 2개세트 마켓이
	- text: 매일 상하치즈 리코타치즈 200g 2개 냉장배송 대명유통
	- text: 홉라 생크림 무가당 1L 휘핑크림 베이킹 쿠킹크림 1000ml 홉라 생크림 무가당 1L + 아이스박스 (주)비오비
	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: 0.9220726783310902
	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: 6 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 \|
	\|:------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 5.0 \| <ul><li>'[아이스박스무료] 선인 DB 휘핑크림 1L 무가당 혼합 생크림 재이F&B'</li><li>'이탈리아 홉라 식물성 생크림 500ml 6개 무가당 홉라 홈라 크림 알이즈웰'</li><li>'카파 포모나 휘핑 스프레이 500g 와플 크림 딸기향/휘핑 스프레이 500g 디씨즈(This is)'</li></ul> \|
	\| 1.0 \| <ul><li>'오뚜기 버터후레시 48개(아이스박스)/일회용버터 오뚜기 딸기잼 디스펜팩 40개+메이플시럽 40 박길용'</li><li>'버터린 롯데 450g 마늘향오일 갈릭버터오일 (주)인벨'</li><li>'[본사직송] 라꽁비에뜨 가염 무염 꽃소금 버터 450g (15g x 30개) 11/15(수)배송예정_라꽁비에뜨-가염 450g (30개입) 인에이블 코리아(주)'</li></ul> \|
	\| 3.0 \| <ul><li>'인도네시아 인도 밀크 스위트드컨덴센 팩 545g 연유 동원무역(이마트24 감천네거리점)'</li><li>'매일연유 5kg 대용량 x 2개 연유 카페스토리(CAFE STORY)'</li><li>'누티 크리머 스위텐드 연유 시럽 385g x 8개 클루'</li></ul> \|
	\| 0.0 \| <ul><li>'오뚜기 파운드 마아가린(벌크) 9kg 피치피치몰'</li><li>'오뚜기 쿠키 옥수수마가린 200Gx2 1세트 제과 제빵 토스트 방글방글마켓'</li><li>'Whirl Admiration Pro Fry 액체 쇼트닝 튀김용 3.6kg 8파운드 포커스라이프'</li></ul> \|
	\| 2.0 \| <ul><li>'밀락골드 1L 제품수량선택 에스제이푸드(SJ FOOD)'</li><li>'[구매전 긴급공지 필독]1217. 뉴골드라벨 - 한박스(1030g x 12개) 베이킹도전'</li><li>'밀락골드 1L 아이스박스필수구매 에스제이푸드(SJ FOOD)'</li></ul> \|
	\| 4.0 \| <ul><li>'상하 샐러드용 슈레드치즈 210g X 1개 종이박스포장 오하'</li><li>'끼리 크림치즈 스프레드 플레인 x 4개 베이글 발라 먹는 치즈 토스트 끼리 크림치즈 스프레드 플레인 4개 더팜'</li><li>'데르뜨 롤케이크 선물세트 소잘우유 초코크림 380g 1개 우유크림 360g 냉동 오씨홀딩스'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Metric \|
	\|:--------\|:-------\|
	\| all \| 0.9221 \|

	## 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_fd13")
	# Run inference
	preds = model("홉라 무가당 휘핑크림 1L 2개세트 마켓이")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 3 \| 9.5067 \| 18 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0.0 \| 50 \|
	\| 1.0 \| 50 \|
	\| 2.0 \| 50 \|
	\| 3.0 \| 50 \|
	\| 4.0 \| 50 \|
	\| 5.0 \| 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0213 \| 1 \| 0.4249 \| - \|
	\| 1.0638 \| 50 \| 0.2783 \| - \|
	\| 2.1277 \| 100 \| 0.0747 \| - \|
	\| 3.1915 \| 150 \| 0.0734 \| - \|
	\| 4.2553 \| 200 \| 0.0368 \| - \|
	\| 5.3191 \| 250 \| 0.0373 \| - \|
	\| 6.3830 \| 300 \| 0.0003 \| - \|
	\| 7.4468 \| 350 \| 0.0001 \| - \|
	\| 8.5106 \| 400 \| 0.0001 \| - \|
	\| 9.5745 \| 450 \| 0.0001 \| - \|
	\| 10.6383 \| 500 \| 0.0 \| - \|
	\| 11.7021 \| 550 \| 0.0 \| - \|
	\| 12.7660 \| 600 \| 0.0 \| - \|
	\| 13.8298 \| 650 \| 0.0 \| - \|
	\| 14.8936 \| 700 \| 0.0 \| - \|
	\| 15.9574 \| 750 \| 0.0 \| - \|
	\| 17.0213 \| 800 \| 0.0 \| - \|
	\| 18.0851 \| 850 \| 0.0 \| - \|
	\| 19.1489 \| 900 \| 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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