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:

Fine-tuning a Sentence Transformer with contrastive learning.
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	'파쉬 독일 보온 물주머니 노커버 기본형 커버 체크 핑크네이비 주식회사 하이유로' '파쉬 독일 보온 물주머니 노커버 기본형 3.노커버 기본형 레드 주식회사 하이유로' '꼼띠아 국산 프리미엄 온열 황토 순면 냉 온 어깨 찜질기 찜질팩 목 등 찜질 쿨매트 허리찜질기(그레이) BH스토어'
0.0	'한양 온수찜질기 밍크 파우치 회색_SET 밍크파우치+복대 한양의료기' '한양 온수찜질기 밍크 파우치 블랙_밍크 발찜질기 한양의료기' '게르마늄 전기찜질기 뜸질기 찜질기 찜질팩 전기메트 허리 배 무릎 찜질 MinSellAmount 스마일배송'

Evaluation

Metrics

Label	Metric
all	0.9710

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_lh5")
# Run inference
preds = model("충전식 온수 찜질기 온열 전기 찜질팩 IVB-D1000 핑크 ")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	4	10.73	20

Label	Training Sample Count
0.0	50
1.0	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
0.0625	1	0.3748	-
3.125	50	0.0002	-
6.25	100	0.0	-
9.375	150	0.0	-
12.5	200	0.0	-
15.625	250	0.0	-
18.75	300	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}
}

mini1013
/

master_cate_lh5