mini1013/master_cate_lh19

SetFit with mini1013/master_domain

This is a SetFit model that can be used for Text Classification. This SetFit model uses mini1013/master_domain as the Sentence Transformer embedding model. A LogisticRegression 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 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
• Classification head: a LogisticRegression instance
• Maximum Sequence Length: 512 tokens
• Number of Classes: 5 classes

Model Sources

• Repository: SetFit on GitHub
• Paper: Efficient Few-Shot Learning Without Prompts
• Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Model Labels

Label	Examples
2.0	'세운 네라톤카테타 #1116 라텍스 멸균 100개 팩 6번 12fr 4.0mm0 트리비즈니스' '세운 바로박(Barovac) PS200C 단위:1개 (주)엠디오씨' '의무실 성인용 고무밴드 네블라이저 마스크 호흡기 흡입마스크 기관지 인사이트쇼핑몰'
1.0	'JW중외제약 하이맘밴드 프리미엄 2매 이지덤(대웅제약)_이지덤씬 2매(+가위) 테크노 제일약국' '메디폼 친수성 폼드레싱 10x10cm (5mm) (2mm) 10매입 1박스 5mm 주식회사 엠퍼러' '메나리니 더마틱스 울트라 겔 15g 1개. 릴리뷰티'
0.0	'약국 에탄올스왑 일회용 알콜솜 에프에이 이올스왑 알콜스왑 소독솜 1박스 다팜메디' '[유한양행] 해피홈 소독용 알콜스왑알콜솜 100매입 2개 [0001]기본상품 CJONSTYLE' '일회용 알콜솜 알콜스왑 소독 약국 바른케어 개별포장100매 바른케어 플러스 알콜솜 100매 로그엠(LOGM)'
4.0	'가주 비멸균 설압자 1통(100개) 혀누르개 목설압자 의료용 병원용 더블세이프 MinSellAmount 이원헬스케어' '의료용 겸자 12.5cm /곡 모스키토 켈리 포셉 SJ헬스케어' '개부밧드6절(뚜껑있는밧드)소독통/개무밧드/사각트레이/트레이밧드/거어즈캔 신동방메디칼'
3.0	'일회용 베드 위생시트 부직포시트 침대커버 1롤 50장 80x180cm 비방수(고급형) 80x180 50장/롤 심비오시스' '부직포자루,육수보자기,다시백,거름망 45x50-300장 봉제 지우씨' '병원침대/환자용침대 매트리스/고탄성 접이식 마사지 지압 의료용 매트 두께 9cm_밤색 평매트리스_900mm X 1900mm 메디칼베드마트'

Evaluation

Metrics

Label	Metric
all	0.9571

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("mini1013/master_cate_lh19")
# Run inference
preds = model("[저소음 미세입자] 오므론 네블라이저 NE-C803  꿈꾸는약국")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	3	10.084	20

Label	Training Sample Count
0.0	50
1.0	50
2.0	50
3.0	50
4.0	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
0.025	1	0.4162	-
1.25	50	0.2435	-
2.5	100	0.0066	-
3.75	150	0.0054	-
5.0	200	0.0001	-
6.25	250	0.0	-
7.5	300	0.0	-
8.75	350	0.0	-
10.0	400	0.0	-
11.25	450	0.0	-
12.5	500	0.0	-
13.75	550	0.0	-
15.0	600	0.0	-
16.25	650	0.0	-
17.5	700	0.0	-
18.75	750	0.0	-
20.0	800	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

@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}
}