mini1013/master_cate_ac5

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: 3 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
0.0	'[한국표준금거래소] 999.9‰순금 골드바 11.25g 쇼핑백X (주)한국표준거래소' '한국금거래소 순금 꽃다발 골드바 0.2g 기본 종이 케이스 한국금거래소디지털에셋' '한국금거래소 순금 비상금 통장 골드바 1g 주식회사 한국금거래소디지털에셋'
1.0	'[한국금거래소]한국금거래소 순금 복주머니 3.75g 롯데아이몰' '[한국금거래소] 어락도 금수저 카드 3.75g 주식회사 한국금거래소디지털에셋' '순금거북이 37.5g 종로골드'
2.0	'[한국금거래소] 실버바 100g 은테크 은투자 은시세 생일 기념일 축하 선물 주식회사 한국금거래소디지털에셋' '[100g 실버바] 한국금거래소 99.99% 투자용 은괴 주식회사 골드나라' '[삼성금거래소]Silver Bar(실버바)100g AKmall'

Evaluation

Metrics

Label	Metric
all	0.9977

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_ac5")
# Run inference
preds = model("순금뱃지 1.875g 기업 회사 은행 병원 대학교 금뱃지 2.금형추가 투자골드")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	4	7.7583	17

Label	Training Sample Count
0.0	50
1.0	50
2.0	20

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
0.0526	1	0.4971	-
2.6316	50	0.0373	-
5.2632	100	0.0001	-
7.8947	150	0.0	-
10.5263	200	0.0	-
13.1579	250	0.0	-
15.7895	300	0.0	-
18.4211	350	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}
}