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: 4 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
3.0	'찹쌀호떡믹스 400g 5개 오브젝티브' '신진 찹쌀호떡가루 2.5Kg 호떡믹스 퍼스트' '찹쌀호떡믹스 400g 10개 묶음배송가능 옵션9.\xa0오븐용깨찰빵믹스 500g EY 인터내셔널'
0.0	'브레드가든 바닐라에센스 59ml 주식회사 몬즈컴퍼니' '선인 냉동레몬제스트 500g 레몬껍질 선인 냉동레몬제스트 500g 레몬껍질 아이은하' '샤프 인스턴트 이스트 골드 500g 샤프 이스트 골드 500g 주식회사 맘쿠킹'
2.0	'곰표 와플믹스 1kg x 4팩 코스트코나' '동원비셰프 스위트사워믹스1kg 엠디에스마케팅 주식회사' 'CJ 백설 붕어빵믹스 10kg [맛있는] [좋아하는]간편 로이스'
1.0	'오뚜기 베이킹소다 400g 지윤 주식회사' '밥스레드밀 파우더 397g 베이킹 글로벌피스' 'Anthony s 유기농 요리 등급 코코아 파우더 1 lb 프로마스터'

Evaluation

Metrics

Label	Metric
all	0.8175

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_fd17")
# Run inference
preds = model("행복한 쌀잉어빵 반죽 5kg 팥앙금 3kg 행복유통")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	3	9.2	22

Label	Training Sample Count
0.0	50
1.0	50
2.0	50
3.0	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
0.0312	1	0.4064	-
1.5625	50	0.1639	-
3.125	100	0.003	-
4.6875	150	0.0003	-
6.25	200	0.0001	-
7.8125	250	0.0001	-
9.375	300	0.0001	-
10.9375	350	0.0	-
12.5	400	0.0	-
14.0625	450	0.0	-
15.625	500	0.0	-
17.1875	550	0.0	-
18.75	600	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_fd17