master_cate_bc27 / README.md

Push model using huggingface_hub.

1b2aaea verified 7 days ago

13.4 kB

	---
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: '[1300K] 국내생산 뉴니끄 후크랩 솔리드 수유 브라&드로즈팬티 세트 샌드베이지_브라(L)/팬티(M-L) 출산/육아 > 임부복 >
	임부속옷 > 수유브라'
	- text: 반팔 부엉이레이스티 여성의류 임부복 임산부티셔츠 출산/육아 > 임부복 > 수유복
	- text: 외출수유원피스 산후조리원복 산모복 수유외출복 그레이(L) 출산/육아 > 임부복 > 수유복
	- text: My Bump 여성용 하이 웨이스트 바닥 길이 임산부 맥시 스커트 정품보장 X-Large_Mocha Sd 출산/육아 > 임부복 > 수유복
	- text: 여성 임산부 운동복 쫄 바지 배꼽 아래 레깅스 선물 저렴한 요가복 부쫄 배꼽아래 부 임산 다크그레이 M 출산/육아 > 임부복 > 바지
	metrics:
	- accuracy
	pipeline_tag: text-classification
	library_name: setfit
	inference: true
	base_model: mini1013/master_domain
	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: accuracy
	value: 1.0
	name: Accuracy
	---

	# 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>'반팔퍼프 모노키니 임부복수영복 셔링비키니 빅사이즈 화이트, 블랙 (M,L,XL) 블랙_L 출산/육아 > 임부복 > 임부용수영복'</li><li>'2024 새로운 임산부 수영복 배꼽 커버 큰 연꽃 잎 가장자리 한 어깨 원피스 수영복 기초 잎_XXXL 출산/육아 > 임부복 > 임부용수영복'</li><li>'임산부래쉬가드 임산부수영복 체형커버 빅사이즈 만삭 블루_S 출산/육아 > 임부복 > 임부용수영복'</li></ul> \|
	\| 4.0 \| <ul><li>'보리맘 투투 반팔 원피스 세트 롱원피스 임부복 R414 출산/육아 > 임부복 > 원피스'</li><li>'고급스러운 카라 브이넥 부드러운 니트 페이크버튼 임산부원피스 임부복원피스 만삭임부복 블랙_Free 출산/육아 > 임부복 > 원피스'</li><li>'출산 전후 임산부 골반 복대 벨트 B_엘 출산/육아 > 임부복 > 원피스'</li></ul> \|
	\| 3.0 \| <ul><li>'9022 2023 용수철 여름 주름 임산부 스커트 신축성 허리 뱃살 의류 임산부 하의 출산/육아 > 임부복 > 스커트'</li><li>'가을 코디 투피스 니트 스커트 탑 스웨터 원피스 나른한 세트 임부스커트-블랙_L 출산/육아 > 임부복 > 스커트'</li><li>'임산부 스커트 임부복 치마 가을 겨울 벨벳 A 라인 빅사이즈 플리츠 편안한 블랙레귤러 스타일_XXL 출산/육아 > 임부복 > 스커트'</li></ul> \|
	\| 1.0 \| <ul><li>'임부복 썸머플리츠 임산부반바지 출산/육아 > 임부복 > 바지'</li><li>'뉴니끄 임산부 빅사이즈 수유브라 수유나시 팬티 임산부내의 임산부 손목보호대 일반형(2p) 텐셀랩 임산부 드로즈팬티_파스텔블루_M-L 출산/육아 > 임부복 > 바지'</li><li>'AMPOSH 여성용 임산부운동복 바지 신축성 임신 조거 팬츠 보온츄리닝 트레이닝복 헤더버건디_XL 출산/육아 > 임부복 > 바지'</li></ul> \|
	\| 0.0 \| <ul><li>'임산부 원피스 임부복 배 지지 레깅스 출산/육아 > 임부복 > 레깅스'</li><li>'원피스 임산부 임부복 와이드 벨트 배 지지 레깅스 출산/육아 > 임부복 > 레깅스'</li><li>'임산부 겨울 레깅스 겨울용 두꺼운 임산부용 러블리 쇼 얇은 바지 파일 패브릭 510g 04 golden blue_03 XXL 출산/육아 > 임부복 > 레깅스'</li></ul> \|
	\| 5.0 \| <ul><li>'마마조이 심리스 에어 수유브라 그레이_L 출산/육아 > 임부복 > 임부속옷 > 수유브라'</li><li>'수유나시 원터치 임산부 임부 속옷 잠옷 수유복 산모 내의 블루_2XL 출산/육아 > 임부복 > 임부속옷 > 임부러닝'</li><li>'쌍방울 TRY 마더마인드 9부 면스판 임산부 내복 272 상하 1세트 TW9S272 피치_000 (Free) 출산/육아 > 임부복 > 임부속옷 > 임부내복'</li></ul> \|
	\| 2.0 \| <ul><li>'Summer Mae 임산부 수영복 원피스 수영복 버튼 넥 크로스 백 정품보장 Large_Purple 출산/육아 > 임부복 > 수유복'</li><li>'랭글러 Wrangler 여성용 레트로 Mae 임산부 부츠 컷 진 정품보장 Denim_0-34 출산/육아 > 임부복 > 수유복'</li><li>'반폴라 봄 축열덕융세트 레깅스 상의 티셔츠 이너 가을겨울 3XL[72.5-82.5kg 권장]_카멜반폴라[가을겨울 보온] 출산/육아 > 임부복 > 수유복'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 1.0 \|

	## 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_bc27")
	# Run inference
	preds = model("반팔 부엉이레이스티 여성의류 임부복 임산부티셔츠 출산/육아 > 임부복 > 수유복")
	```

	<!--
	### 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 \| 8 \| 15.0776 \| 33 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0.0 \| 70 \|
	\| 1.0 \| 70 \|
	\| 2.0 \| 70 \|
	\| 3.0 \| 70 \|
	\| 4.0 \| 70 \|
	\| 5.0 \| 70 \|
	\| 6.0 \| 70 \|

	### Training Hyperparameters
	- batch_size: (256, 256)
	- num_epochs: (30, 30)
	- max_steps: -1
	- sampling_strategy: oversampling
	- num_iterations: 50
	- body_learning_rate: (2e-05, 1e-05)
	- head_learning_rate: 0.01
	- loss: CosineSimilarityLoss
	- distance_metric: cosine_distance
	- margin: 0.25
	- end_to_end: False
	- use_amp: False
	- warmup_proportion: 0.1
	- l2_weight: 0.01
	- seed: 42
	- eval_max_steps: -1
	- load_best_model_at_end: False

	### Training Results
	\| Epoch \| Step \| Training Loss \| Validation Loss \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0104 \| 1 \| 0.4946 \| - \|
	\| 0.5208 \| 50 \| 0.4988 \| - \|
	\| 1.0417 \| 100 \| 0.348 \| - \|
	\| 1.5625 \| 150 \| 0.1457 \| - \|
	\| 2.0833 \| 200 \| 0.0479 \| - \|
	\| 2.6042 \| 250 \| 0.0175 \| - \|
	\| 3.125 \| 300 \| 0.0002 \| - \|
	\| 3.6458 \| 350 \| 0.0001 \| - \|
	\| 4.1667 \| 400 \| 0.0001 \| - \|
	\| 4.6875 \| 450 \| 0.0 \| - \|
	\| 5.2083 \| 500 \| 0.0 \| - \|
	\| 5.7292 \| 550 \| 0.0 \| - \|
	\| 6.25 \| 600 \| 0.0 \| - \|
	\| 6.7708 \| 650 \| 0.0 \| - \|
	\| 7.2917 \| 700 \| 0.0 \| - \|
	\| 7.8125 \| 750 \| 0.0 \| - \|
	\| 8.3333 \| 800 \| 0.0 \| - \|
	\| 8.8542 \| 850 \| 0.0 \| - \|
	\| 9.375 \| 900 \| 0.0 \| - \|
	\| 9.8958 \| 950 \| 0.0 \| - \|
	\| 10.4167 \| 1000 \| 0.0 \| - \|
	\| 10.9375 \| 1050 \| 0.0 \| - \|
	\| 11.4583 \| 1100 \| 0.0 \| - \|
	\| 11.9792 \| 1150 \| 0.0 \| - \|
	\| 12.5 \| 1200 \| 0.0 \| - \|
	\| 13.0208 \| 1250 \| 0.0 \| - \|
	\| 13.5417 \| 1300 \| 0.0 \| - \|
	\| 14.0625 \| 1350 \| 0.0 \| - \|
	\| 14.5833 \| 1400 \| 0.0 \| - \|
	\| 15.1042 \| 1450 \| 0.0 \| - \|
	\| 15.625 \| 1500 \| 0.0 \| - \|
	\| 16.1458 \| 1550 \| 0.0 \| - \|
	\| 16.6667 \| 1600 \| 0.0 \| - \|
	\| 17.1875 \| 1650 \| 0.0 \| - \|
	\| 17.7083 \| 1700 \| 0.0 \| - \|
	\| 18.2292 \| 1750 \| 0.0 \| - \|
	\| 18.75 \| 1800 \| 0.0 \| - \|
	\| 19.2708 \| 1850 \| 0.0 \| - \|
	\| 19.7917 \| 1900 \| 0.0 \| - \|
	\| 20.3125 \| 1950 \| 0.0 \| - \|
	\| 20.8333 \| 2000 \| 0.0 \| - \|
	\| 21.3542 \| 2050 \| 0.0 \| - \|
	\| 21.875 \| 2100 \| 0.0 \| - \|
	\| 22.3958 \| 2150 \| 0.0 \| - \|
	\| 22.9167 \| 2200 \| 0.0 \| - \|
	\| 23.4375 \| 2250 \| 0.0 \| - \|
	\| 23.9583 \| 2300 \| 0.0 \| - \|
	\| 24.4792 \| 2350 \| 0.0 \| - \|
	\| 25.0 \| 2400 \| 0.0 \| - \|
	\| 25.5208 \| 2450 \| 0.0 \| - \|
	\| 26.0417 \| 2500 \| 0.0 \| - \|
	\| 26.5625 \| 2550 \| 0.0 \| - \|
	\| 27.0833 \| 2600 \| 0.0 \| - \|
	\| 27.6042 \| 2650 \| 0.0 \| - \|
	\| 28.125 \| 2700 \| 0.0 \| - \|
	\| 28.6458 \| 2750 \| 0.0 \| - \|
	\| 29.1667 \| 2800 \| 0.0 \| - \|
	\| 29.6875 \| 2850 \| 0.0 \| - \|

	### Framework Versions
	- Python: 3.10.12
	- SetFit: 1.1.0
	- Sentence Transformers: 3.3.1
	- Transformers: 4.44.2
	- PyTorch: 2.2.0a0+81ea7a4
	- Datasets: 3.2.0
	- Tokenizers: 0.19.1

	## 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.
	-->