master_cate_ac0 / README.md

Push model using huggingface_hub.

1cd4b5e verified 4 months ago

12.7 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: '[헤지스ACC]HJBA3F885BK[13인치 노트북 수납가능][KEVIN]블랙 참장식 크로스 겸용 미니 토트백 에이케이에스앤디 (주)
	AK인터넷쇼핑몰'
	- text: 마젤란 메신저백 크로스백 슬링백 힙색 힙쌕 학생 여성 남자 캐주얼 크로스 여행용 여권 핸드폰 보조 학원 가방 LKHS-304_B-연핑크(+키홀더)
	더블유팝
	- text: 마젤란 메신저백 크로스백 슬링백 힙색 힙쌕 학생 여성 남자 캐주얼 크로스 여행용 여권 핸드폰 보조 학원 가방 ML-1928_연그레이
	더블유팝
	- text: '[갤러리아] JUBA4E021G2 [MATEO] 그레이 로고프린트 숄더백 JUBA4E021G2 [MATEO] 그레이 로고프린트 숄더백
	NS홈쇼핑_NS몰'
	- text: '[디스커버리](신세계강남점)[23N] 디스커버리 미니 슬링백 (DXSG0043N) IVD 다크 아이보리_F 주식회사 에스에스지닷컴'
	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: 0.8488667448221962
	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: 9 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>'[질스튜어트](광주신세계)블랙 클래식 클러치백 [JUWA2F392BK] 주식회사 에스에스지닷컴'</li><li>'심플 클러치백 EOCFHX257BK/에스콰이아 블랙 롯데쇼핑(주)'</li><li>'[듀퐁] 소프트그레인 파우치 베이지 CG180263CL 베이지 (주)씨제이이엔엠'</li></ul> \|
	\| 3.0 \| <ul><li>'엔지니어드가먼츠 블랙 나일론 토트백 23F1H034BLACK 주식회사 어도어럭스'</li><li>'[가이거] 퀼팅 레더 체인 숄더백 (+플랩지갑) 캐러멜 브라운 (주)우리홈쇼핑'</li><li>'토트 브리프 크로스백 FT8570 블랙 글로리홈'</li></ul> \|
	\| 4.0 \| <ul><li>'여자캔버스 가방 코디 크로스백 남자에코백 신발 BLUE 고앤런'</li><li>'여학생 에코백 아이보리 가방 남녀공용 캐주얼 쇼퍼백 엘케이엠'</li><li>'패션 에코백 데일리 가방 캐주얼 숄더백 브라운 심정'</li></ul> \|
	\| 7.0 \| <ul><li>'[갤러리아] 644040 2BKPI 1000 ONE SIZE 한화갤러리아(주)'</li><li>'[갤러리아] 헤지스핸드백 그린 워싱가죽 크로스 겸용 토트백 HJBA3E301E2(타임월드) 한화갤러리아(주)'</li><li>'[메종키츠네] 로고 프린트 코튼 토트백 블루 LW05102WW0008 BLUE_FREE 신세계몰'</li></ul> \|
	\| 2.0 \| <ul><li>'바버 가죽 코팅 서류 가방 브리프 케이스 UBA0004 NAVY 뉴욕트레이딩'</li><li>'[롯데백화점]에스콰이아 23FW 신상 경량 나일론 노트북 수납 남여 데일리 토트 크로스백 EOCFHX258BK 롯데백화점_'</li><li>'22FW 신상 뉴 포멀 슬림 스퀘어 심플 비즈니스 캐주얼 서류가방 ECBFHX227GY 롯데백화점1관'</li></ul> \|
	\| 1.0 \| <ul><li>'NATIONALGEOGRAPHIC N225USD340 다이브 플러스 V3 BLACK 240 맥스투'</li><li>'레스포삭 보이저 백팩 경량 나일론 보부상 복조리 가방 7839 플라워 행운샵'</li><li>'레스포삭 보이저 백팩 경량 Voyager Backpack 7839 블랙 하하대행'</li></ul> \|
	\| 0.0 \| <ul><li>'[갤러리아] 헤지스핸드백HJBA2F770BK_ 블랙 로고 장식 솔리드 메신져백(타임월드) 한화갤러리아(주)'</li><li>'로아드로아 허쉬 메쉬 포켓 크로스 메신저백 (아이보리) 크로스백 FREE 가방팝'</li><li>'[본사공식] 타프 메신저백 사첼 S EOCBS04 008 롯데아이몰'</li></ul> \|
	\| 5.0 \| <ul><li>'팩세이프 가방 GO 크로스바디 백 2.5L / PACSAFE URBAN 도난방지 유럽 해외 여행 등산 슬링백 크로스백 RFID차단 1. 제트 블랙 (JET BLACK) 시계1위팝워치'</li><li>'샨타코[Chantaco] 레더 크로스백 BB NH3271C53N 000/라코스테 롯데쇼핑(주)'</li><li>'팩세이프 가방 GO 크로스바디 백 2.5L / PACSAFE URBAN 도난방지 유럽 해외 여행 등산 슬링백 크로스백 RFID차단 2. 로즈 (ROSE) 시계1위팝워치'</li></ul> \|
	\| 8.0 \| <ul><li>'[기회공작소] 데일리 슬링백 크로스 힙색 허리가방 스포츠 등산 힙색 허리색 슬링백 보조가방 글로리커머스'</li><li>'구찌 GG 캔버스 투웨이 밸트백 힙색 630915 KY9KN 9886 쏠나인'</li><li>'벨트형 핸드폰 허리가방 남자 벨트백 세로형 가죽 벨트파우치 지갑 허리벨트케이스 브라운 자주구매'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Metric \|
	\|:--------\|:-------\|
	\| all \| 0.8489 \|

	## 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_ac0")
	# Run inference
	preds = model("[디스커버리](신세계강남점)[23N] 디스커버리 미니 슬링백 (DXSG0043N) IVD 다크 아이보리_F 주식회사 에스에스지닷컴")
	```

	<!--
	### 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 \| 4 \| 9.2289 \| 29 \|

	\| 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 \|
	\| 7.0 \| 50 \|
	\| 8.0 \| 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0141 \| 1 \| 0.3958 \| - \|
	\| 0.7042 \| 50 \| 0.3012 \| - \|
	\| 1.4085 \| 100 \| 0.1811 \| - \|
	\| 2.1127 \| 150 \| 0.0599 \| - \|
	\| 2.8169 \| 200 \| 0.0333 \| - \|
	\| 3.5211 \| 250 \| 0.0169 \| - \|
	\| 4.2254 \| 300 \| 0.0005 \| - \|
	\| 4.9296 \| 350 \| 0.0003 \| - \|
	\| 5.6338 \| 400 \| 0.0002 \| - \|
	\| 6.3380 \| 450 \| 0.0003 \| - \|
	\| 7.0423 \| 500 \| 0.0001 \| - \|
	\| 7.7465 \| 550 \| 0.0001 \| - \|
	\| 8.4507 \| 600 \| 0.0001 \| - \|
	\| 9.1549 \| 650 \| 0.0001 \| - \|
	\| 9.8592 \| 700 \| 0.0001 \| - \|
	\| 10.5634 \| 750 \| 0.0 \| - \|
	\| 11.2676 \| 800 \| 0.0001 \| - \|
	\| 11.9718 \| 850 \| 0.0001 \| - \|
	\| 12.6761 \| 900 \| 0.0001 \| - \|
	\| 13.3803 \| 950 \| 0.0 \| - \|
	\| 14.0845 \| 1000 \| 0.0 \| - \|
	\| 14.7887 \| 1050 \| 0.0 \| - \|
	\| 15.4930 \| 1100 \| 0.0 \| - \|
	\| 16.1972 \| 1150 \| 0.0 \| - \|
	\| 16.9014 \| 1200 \| 0.0 \| - \|
	\| 17.6056 \| 1250 \| 0.0 \| - \|
	\| 18.3099 \| 1300 \| 0.0 \| - \|
	\| 19.0141 \| 1350 \| 0.0 \| - \|
	\| 19.7183 \| 1400 \| 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.
	-->