master_cate_lh17 / README.md

Push model using huggingface_hub.

fabdeb1 verified 2 days ago

10.1 kB

	---
	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: 트라택 마사지건 액티브건 팟 휴대용 초소형 김계란 근육 무선마사지 코발트블루&차콜 주식회사 나음케어
	- text: 접이식 도수 치료 추나 테이블 경락 안마 피부샵 베드 미용 침대 마사지 관리 보라색 70cm 침대+침공베개 레비하이
	- text: 케잔 괄사 마사지 승모근 어깨 림프순환 괄사 세라믹괄사 1. 옵션1 강한자극 케잔아일랜드
	- text: 접이식 마사지 침대 피부관리 한의원 안마 미용베드 11.70cm 와이드 2단 그레이 서진홀딩스
	- text: 등 허리 경추 다기능 전신 목 어깨 전기 마사지 쿠션 허리안마기 A_EU 파밀리아
	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: 0.8100799016594961
	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 \|
	\|:------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 2.0 \| <ul><li>'멜킨 바른 그래핀 무릎 온열 찜질 마사지기 무선 안마기 찜질마사지기듀얼2개 아이니쥬♥'</li><li>'눈편한세상 눈 안마기 온열안대 공기압 기계 마사지기 오아월드'</li><li>'엘보타파 전완근 무선 온열 공기압 손 마사지기 MDM-1422S 실버 MDM-1422S 메디니스'</li></ul> \|
	\| 6.0 \| <ul><li>'보이로 640.37 MG 260 지압 마사지기 시트 커버 블랙 사무실 의자 시니어 부모님 유로사이드라인'</li><li>'차량용안마기 화물차 기사용 바디 피로 자궁 쿠션 안정기 탑 USB/308E 디럭스 2 오션글림'</li><li>'차량용안마기 화물차 기사용 바디 피로 자궁 쿠션 안정기 탑 USB/308E 디럭스 1 오션글림'</li></ul> \|
	\| 0.0 \| <ul><li>'접이식 도수 치료 추나 테이블 경락 안마 피부샵 베드 미용 침대 마사지 관리 조절형 커피색 60cm 침대+침공베개 레비하이'</li><li>'고급 비닐커버 특대형 침대비닐커버 침대 맞춤 병원 피부샵 경락샵 2000x1300_피부비닐커버(구멍X) skin2010'</li><li>'바이오힐보 프로바이오덤 리프팅 괄사 마사저 림프 마사지기 쿨링 마사저 쿨링 마사저 제이글로벌'</li></ul> \|
	\| 4.0 \| <ul><li>'[ 추가 만원 할인쿠폰 ] 파나소닉 김강우 안마의자 EP-MA05 (2종택1)+/ 카페트 및 무상 AS 1년 화이트&클래식블루 (주)렙테크'</li><li>'[세라젬] 파우제 M4 안마의자 마사지 휴식가전 베이지 세라젬'</li><li>'웰모아 안마의자 공기압마사지기 다리길이 조절 등 허리 온열 HCW-6300 도레미마켓'</li></ul> \|
	\| 3.0 \| <ul><li>'굿프렌드 온열 전신안마매트 GT-S6 GSSHOP_'</li><li>'목욕탕 때밀이 침대 교체용 마사지 베드 매트 쿠션 판 블루 185x60 밀꾸밀꾸'</li><li>'혜성의료기 국내생산 두타매트 HS-770 온열과 안마 받침대옵션 마사지매트 받침대없음_14봉 멸치쇼핑'</li></ul> \|
	\| 1.0 \| <ul><li>'멜킨 멜리즈 마사지건 무선 전동 휴대용 저소음 마사지기 어깨 승모근 전신 안마기 선물 빈티지 아이보리 (주)거성디지털'</li><li>'인썸 휴대용 미니 핸디 전동 마사지건 IMG-150 '</li><li>'멜킨 멜리즈 마사지건 무선 전동 휴대용 저소음 마사지기 어깨 승모근 전신 안마기 선물 제트 블랙 (주)거성디지털'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Metric \|
	\|:--------\|:-------\|
	\| all \| 0.8101 \|

	## 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_lh17")
	# Run inference
	preds = model("등 허리 경추 다기능 전신 목 어깨 전기 마사지 쿠션 허리안마기 A_EU 파밀리아")
	```

	<!--
	### Downstream Use

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

	<!--
	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
	-->

	<!--
	## 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 \| 11.3370 \| 23 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0.0 \| 50 \|
	\| 1.0 \| 20 \|
	\| 2.0 \| 50 \|
	\| 3.0 \| 50 \|
	\| 4.0 \| 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0233 \| 1 \| 0.4557 \| - \|
	\| 1.1628 \| 50 \| 0.2241 \| - \|
	\| 2.3256 \| 100 \| 0.0604 \| - \|
	\| 3.4884 \| 150 \| 0.0172 \| - \|
	\| 4.6512 \| 200 \| 0.0031 \| - \|
	\| 5.8140 \| 250 \| 0.0009 \| - \|
	\| 6.9767 \| 300 \| 0.004 \| - \|
	\| 8.1395 \| 350 \| 0.0001 \| - \|
	\| 9.3023 \| 400 \| 0.0 \| - \|
	\| 10.4651 \| 450 \| 0.0 \| - \|
	\| 11.6279 \| 500 \| 0.0 \| - \|
	\| 12.7907 \| 550 \| 0.0 \| - \|
	\| 13.9535 \| 600 \| 0.0 \| - \|
	\| 15.1163 \| 650 \| 0.0 \| - \|
	\| 16.2791 \| 700 \| 0.0 \| - \|
	\| 17.4419 \| 750 \| 0.0 \| - \|
	\| 18.6047 \| 800 \| 0.0 \| - \|
	\| 19.7674 \| 850 \| 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}
	}
	```

	<!--
	## Glossary

	Clearly define terms in order to be accessible across audiences.
	-->

	<!--
	## Model Card Authors

	Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.
	-->

	<!--
	## Model Card Contact

	Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.
	-->