mini1013/master_cate_sl7

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: 2 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
1.0	'2단 발레바 댄스 무용바 스트레칭 연습 레그 프레스 스포츠/레저>댄스>댄스소품' '발레바 폴댄스봉 실내 가정용 학원 스튜디오 폴봉 폴-D 50 0cm 스포츠/레저>댄스>댄스소품' '댄스 바 스트레칭 무용 플로어 학원 발레 연습 스포츠/레저>댄스>댄스소품'
0.0	'코코랑 맨디 밸리탑 밸리댄스복 라인 줌바 댄스티 밸리복 스포츠/레저>댄스>댄스복' '댄스 힙스카프 라인 스팽글 밸리 랩스커트 공연복 스포츠/레저>댄스>댄스복' '코코랑 코코레이스 벨리 힙스카프 성인 밸리댄스복 라인 스포츠/레저>댄스>댄스복'

Evaluation

Metrics

Label	Accuracy
all	1.0

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_sl7")
# Run inference
preds = model("발레바 발레봉 무용 난간대 스트레칭 학원 무용바 스포츠/레저>댄스>댄스소품")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	4	9.9786	18

Label	Training Sample Count
0.0	70
1.0	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
0.0357	1	0.4782	-
1.7857	50	0.3827	-
3.5714	100	0.0001	-
5.3571	150	0.0	-
7.1429	200	0.0	-
8.9286	250	0.0	-
10.7143	300	0.0	-
12.5	350	0.0	-
14.2857	400	0.0	-
16.0714	450	0.0	-
17.8571	500	0.0	-
19.6429	550	0.0	-
21.4286	600	0.0	-
23.2143	650	0.0	-
25.0	700	0.0	-
26.7857	750	0.0	-
28.5714	800	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

@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}
}